El I Mehdi

El I Baramoussi

Yangang Ren
email: ygren@rcees.ac.cn

Chaoyang Xue

Ibrahim Ouchen

Véronique Daële

Patrick Mercier

Christophe Chalumeau

Frédéric L E Fur

Patrice Colin

Abderrazak Yahyaoui

Frédéric Le Fur

Oliver Favez

Abdelwahid Mellouki
email: mellouki@cnrs-orleans.fr

Nearly five-year continuous atmospheric measurements of black carbon over a

Keywords: Black carbon, measurements, long-term, seasonal pattern, weekend effect, lockdown

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Black carbon (BC) is a primary aerosol emitted directly from incomplete combustion processes such as vehicle exhausts (especially unfiltered diesel type), domestic and industrial coal, heavy oil and wood burning, as well as forest and vegetation fires [START_REF] Who | Sixty-fifth World Heath Assemby, Resolutions and Decisions[END_REF]. As a component of atmospheric particulate matter (PM), BC plays an important role in the aerosol-planetary boundary layer (PBL) interactions that can for example enhance the haze pollution [START_REF] Ding | Enhanced haze pollution by black carbon in megacities in China[END_REF][START_REF] Zhang | An unexpected catalyst dominates formation and radiative forcing of regional haze[END_REF].

BC is recognized as an efficient proxy of negative impacts on human health by causing morbidity and premature mortality [START_REF] Silva | Global premature mortality due to anthropogenic outdoor air pollution and the contribution of past climate change[END_REF]. Because of their small diameter (≤2.5 µm, PM2.5), BC particles can enter deep into the lungs and the bloodstream to cause cardiovascular and respiratory diseases which may lead to premature death [START_REF] Anenberg | Impacts of global, regional, and sectoral black carbon emission reductions on surface air quality and human mortality[END_REF][START_REF] Gong | Associations of black carbon and PM2.5 with daily cardiovascular mortality in Beijing, China[END_REF][START_REF] Li | Assessing public health burden associated with exposure to ambient black carbon in the United States[END_REF][START_REF] Wang | Premature Mortality Associated with Exposure to Outdoor Black Carbon and Its Source Contributions in China[END_REF]. In addition, BC affects visibility and harms ecosystems. BC aerosols play an important role in the climate system, they strongly absorb solar radiation and warm the atmosphere. BC is the second climate forcing agent after CO 2 with a positive radiative forcing of 1.1 W m -2 [START_REF] Pörtner | Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change[END_REF]. Because of its short atmospheric lifetime, estimated to be of a few days to a few weeks, it has been suggested that mitigation of its emissions is one of the most effective strategies for slowing climate change. The climate will respond quickly to reductions of black carbon, especially in remote regions like the arctic where the warming and melting of snow and ice could be slowed [START_REF] Amap | AMAP Assessment 2015: Black carbon and ozone as Arctic climate forcers[END_REF][START_REF] Sand | Response of Arctic temperature to changes in emissions of short-lived climate forcers[END_REF]WMO, 2016) .

Because of its impact on global warming, human health and ecosystems, a large body of research and monitoring activities have been dedicated to the understanding of the atmospheric behavior of BC [START_REF] Cui | One-Year Real-Time Measurement of Black Carbon in the Rural Area of Qingdao, Northeastern China: Seasonal Variations, Meteorological Effects, and the COVID-19 Case Analysis[END_REF][START_REF] Garrido | Levels, trends and health concerns of atmospheric PAHs in Europe[END_REF][START_REF] Kutzner | Long-term monitoring of black carbon across Germany[END_REF][START_REF] Singh | Trends of atmospheric black carbon concentration over the United Kingdom[END_REF] and to take actions to reduce its emissions since it could be considered as a key pollutant to abate from anthropogenic sources.

In the present paper, we report hourly, monthly, and yearly average concentrations and near five years trend of BC levels from September 2017 to December 2021 at the Voltaire supersite at the CNRS Campus (Orléans, France) to trace the major outflow pathways and transport mechanisms for the area of the interest in the present work.

Methodology

Measurement Site

Real-time continuous measurements of BC were made at the Super-Site Voltaire located in the campus of the National Scientific Research Centre (CNRS) in Orléans, France, as shown in Figure 1, about 8 km south of the Orléans city center (47°50′16.80″N, 1°56′39.34″E). The site is mostly surrounded by grass and trees with no obstructing buildings around it within 50 m. In addition to BC, the site is also equipped with instruments to measure O 3 and NO x .

Instrumentation

An aethalometer model AE33-7 (Magee Scientific Inc.) with a PM 2.5 inlet was used to measure aerosol BC in real time from September 2017 to December 2021. The AE33 collects particles continuously by flowing air stream through a filter tape. Air is pumped through an inlet at the desired flow rate of 5.0 L/min. The aerosol analysis, done at seven optical wavelengths (370, 470, 520, 590, 660, 880, 950 nm), is made by measuring the transmission of light through one portion of the filter tape containing the sample, versus the transmission through an unloaded portion of the filter tape which acts as reference. The instrument calculates the instantaneous concentration of optically-absorbing particles from the change of the attenuation of light transmitted through the particle-laden filter. Two measurements are made simultaneously from two sample spots with different rates of accumulation of the sample and the results are combined mathematically to eliminate nonlinearities and provide the compensated particle light absorption and BC mass concentration.

The AE33 calculates BC concentrations with high temporal resolution by analyzing the attenuation of light transmission over time. The BC measurement is extracted from the absorption coefficient measurement at 880 nm. The other wavelengths can be used to estimate the contributions of different combustion sources.

The optical attenuation (ATN) is calculated from the measurement of the light intensity measured through the "clean" filter band (I 0 ) and measured through the particle-laden band (I) using the following mathematical law: ATN = -100 ln(I/I 0 )

The particle attenuation coefficient (b atn ) is calculated by taking into account the area of the measurement (S), the flow rate (F in ) and the sampling time (Δt) between two ATN measurements. Thus the attenuation coefficient, b atn , induced by the particles deposited, expressed in m -1 , is given by: b atn = S × (ΔATN/100)/(F in × Δt)

The flow rate F in is corrected by a leakage factor, ξ, which represents the loss of flow in the optical chamber. It constitutes the difference between the flow measured at the input of the instrument and the actual flow rate passing through the filter:

F in = F out × (1 -ξ)
F out being the measured flow rate and F in the actual flow through the band.

Results and Discussion

Data Overview and Comparisons with Other Sites

The measurements of BC were made from mid-September 2017 up to end December 2021. Technical issues and maintenance of the instruments led to the loss of some data for the period covering January 2018 -December 2021. For this specific period, more than 71% of data has been recorded (Figure S1). Figure 2 shows an overview of measured BC in the present work. In general, the measurement period was characterised by a fairly good air quality in the area of interest to our study, without heavy pollution days. BC was generally lower than 2 µg m -3 with few exceptions reaching 4 µg m -3 . The annual mean atmospheric BC concentration was 0.75 ± 0.65, 0.58 ± 0.44 , 0.54 ± 0.64, 0.48± 0.46 and 0.50 ± 0.72 μg m -3 , respectively, for the years 2017, 2018, 2019, 2020 and 2021 as shown in Figure 3. The annual mean of BC concentrations measured in suburban Orléans city, were 3 to 30 times lower than reported in other larger European and non-European cities as displayed in Table 1. Note that Orléans is a medium size city, with not much industry and traffic and a population of about 120000 inhabitants.

Nearly 5 years trend of BC in Orléans

The

Seasonal variations

The diurnal profile of BC under different seasons at the site for the entire measurement period is depicted in Figure 5. This figure shows that the highest BC concentration occurred in winter, followed by autumn, spring and summer, with seasonal mean (±SD) values of 0.70 ± 0.18, 0.64 ± 0.11, 0.48 ± 0.11 and 0.38 ± 0.04 μg m -3 , respectively. Compared to spring and summer, the elevated BC concentrations in winter and autumn were likely related to the common occurrence of wood burning for household heating and relative lower boundary height due to cold weather, as reported by other studies [START_REF] Fuller | Contribution of wood burning to PM10 in London[END_REF][START_REF] Genberg | Light-absorbing carbon in Europemeasurement and modelling, with a focus on residential wood combustion emissions[END_REF].

Figure 5 indicates also that the diurnal variations of BC in winter and autumn were similar, within one significant morning peak during the morning rush hours and second significant peak in the evening. In spring and summer, BC concentration also increased in the morning and peaked during the morning rushing hours. However, the morning peak shifted from 7:00 (Local time, LT) in spring, summer, and autumn to 8:00 LT in winter. Due to the increasing of boundary layer height or wind speed during the day. The BC concentration starts to decrease after the peak time until 15:00 LT. Then, as shown in Figure 5, BC concentration increased rapidly from 16:00 LT to the peak at 19:00 LT in autumn and winter, which increased slowly from the minimum and did not present significant peak in the evening in spring and summer.

This indicates that the wood burning was most probably the important BC emission

Weekend effects

Overall, the BC concentration had a weekly cycle with slightly lower concentrations on weekends than during weekdays (Figure 6a,b). The daily median BC concentration ranged between 0.4-0.7 µg m -3 and 0.3-0.5 µg m -3 for weekdays and weekends, respectively. The diurnal variations of BC were different between the weekdays and weekends, as shown in Figure 6. The weekdays presented two peaks pattern related to the morning and evening rush hours, but the weekends only showed slight increase in BC concentration from late afternoon. However, both weekdays and weekends had similar diurnal variation with the BC emission from fossil fuel which reveals that vehicle emission was the main BC source during the weekdays and less use of vehicle on weekends can significantly cut down the local BC concentration.

Lower BC concentration on weekends compared to weekdays was also observed at traffic sites in other European cities like Bern and London [START_REF] Reche | New considerations for PM, Black Carbon and particle number concentration for air quality monitoring across different European cities[END_REF] and Zurich [START_REF] Zotter | Evaluation of the absorption Ångström exponents for traffic and wood burning in the Aethalometer-based source apportionment using radiocarbon measurements of ambient aerosol[END_REF]. [START_REF] Kutzner | Long-term monitoring of black carbon across Germany[END_REF] reported negligible weekdays-weekends differences at urban background and industrial sites in Germany.

As shown in Figure 6, a non-traffic source such as domestic heating could be another important BC source in the suburban site of our study.

COVID-19 Lockdown impact

A number of studies have reported changes in the the observed concentration profiles of BC before, during and after the lockdown in some countries. For example, BC concentration was observed. The averaged BC, BC ff and BC wb concentration for these three lockdowns is presented in Figure 7. Interestingly, as shown in Figure 8, we found that BC emission from fossil fuel were at similar levels before, during and after these three lockdown periods, and the BC concentration changes was mainly due to the BC emission from wood burning. The reason could be that the location of the sampling site in this work was not impacted by by heavy traffic and industries. This observation is specific to the investigated site and the situation could be different in other areas such as Delhi [START_REF] Goel | Variations in Black Carbon concentration and sources during COVID-19 lockdown in Delhi[END_REF] and Wuhan (Z. Wang et al., 2021). It is worth noting that in this study we just comparted the observed BC concentrations but did not consider the meteorological impacts (e.g., from atmospheric dilution, boundary layer, etc.), which may add uncertainties.

Wood Burning vs. Fossil Fuel

To further discuss the BC sources in the suburban area of this work, a monthly average of BC wb and BC ff concentration and their contribution to total BC was depicted in Figure 9. The BC concentration peaked in late fall (November) and winter with maxima around 0.86 µg m -3 . As shown in Figure 9A, BC from the combustion of fossil fuel (BC ff ) showed a relatively flat seasonal cycle, roughly constant in the range 0.29 to 0.50 µg m -3 throughout the year 2017 to 2021. To the contrast, BC from the -3 ). Consquently, BC ff made dominate relative contribution to the observed BC throughout the whole year (>50%, Figure 9B), particularly high contributions from May to September (>88%, Figure 9B). This result is in good agreement with other reports indicationg that in the majority of European cities, BC typically originates from traffic [START_REF] Kutzner | Long-term monitoring of black carbon across Germany[END_REF].

As discussed in the literatures [START_REF] Becerril-Valle | Characterization of atmospheric black carbon and co-pollutants in urban and rural areas of Spain[END_REF][START_REF] Kumar | Evaluation of spatial and temporal heterogeneity of black carbon aerosol mass concentration over India using three year measurements from IMD BC observation network[END_REF][START_REF] Mousavi | Spatio-temporal trends and source apportionment of fossil fuel and biomass burning black carbon (BC) in the Los Angeles Basin[END_REF] the monthly variation of BC ff concentration in the year is mainly related to the weather conditions while that of BC bb concentration is not only related to the weather conditions but also human activities, such as the straw burning in autumn, forest fire in summer and wood burning from domestic heating in winter. In this work (Figure 9), BC from the combustion of wood burning (BC wb ) showed a more pronounced seasonal dependence, within negligible contribution to BC from May to September. The BC bb concentrations tend to be higher in the cold seasons to contribute 22-50% to the total BC, which was likely related to the household heating, especially in the evening of winter as shown in Figure S2.

Conclusions and Implications

To investigate the variation and the long-term trend of BC, a nearly five-year BC measurement was conducted in a suburban area in central France. The BC annual mean concentration was calculated as 0.75 ± 0.65, 0.58 ± 0.44, 0.54 ± 0.64, 0.48± 0.46 and 0.50 ± 0.72 μg m -3 , respectively, for the year of 2017, 2018, 2019, 2020 and 2021, indicating a stability to a very slight decrease trend (-0.036 µg m -3 year -1 ) from 2017 to 2021. These BC concentrations were lower than that reported in some other cities. Strong BC seasonal variations were observed, with the highest BC concentrations occurred in winter, followed by autumn, spring and summer. A weekend effect was also observed in the measured area of this work, within generally slight high BC concentration during the workdays. Lower BC concentration was always observed during the weekends which was attributed to less traffic.

As for the COVID-19 measures impact, three successive lockdown periods have been followed in France, two in warm season (March to May) and one in cold season (October to December). We found that the BC concentration decreased during the lockdown in the warm season, but unexpectedly increased during the lockdown in cold season, which was mainly caused by the variation of BC emission from biomass burning.

Fossil fuel contributed more than 90% of BC emission in the summer, much higher than that from wood burning. However, wood burning contributed equivalent (50%) to fossil fuel burning to the BC emission in the cold seasons, revealling the significant impact of winter heating from wood burning on regional air quality.

Moreover, the above findings indicate that local anthoropogenic emissions could strongly affect the BC concentration observed at the VOLTAIRE supersite as well as air quality in this region. Hence, the national level of strategies to improve air quality is always important, but also the local strategies toward reduceing local emission such as domestic wood burning and fossil fuel combustion emissions.

Credit authorship contribution statement

EI Mehdi EI Baramoussi: Investigation, Formal analysis, Writing -original draft.

Yangang Ren: Supervision, Investigation, Formal analysis, Writing -original draft.

Declaration of competing interest

The author declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figure 1 .

 1 Figure 1. Screenshot of the Voltaire sampling site at the CNRS campus (Orléans-France, 47°50′16.80″N, 1°56′39.34″E).

  On the other hand, the attenuation coefficient b atn is corrected for the amount of light scattered by the filter strip. Thus, the absorption coefficient of the particles, b abs , represents the amount of light absorbed by the particles: b abs = b atn /C C is the diffusion coefficient of the filter strip. Hence BC concentration will be obtained from : BC = b abs /σ air σ air is the mass aborption cross-section, within loading effect compensation, BC = BC measured /(1 -k*ATN) k is the compensation parameter, and the final equation: BC = BC emissions could be apportioned to the source of biomass burning (BC bb ) and fossil fuel (BC ff ) based on the Sandradewi et al. (2008) model. This model is based on the difference in absorption coefficient wavelength dependencies, with optical absorption coefficient being a sum of biomass burning and fossil fuel burning fractions. Hence BC bb and BC ff are calculated as: BC bb =BB*BC BC ff = BC*(1-BB) With BB (%) is the portion of biomass burning related to total BC, and calculated using a series of equations: b abs (470 nm) ff / b abs (470 nm) ff = (470/950) -aff b abs (470 nm) bb / b abs (470 nm) bb = (470/950) -abb b abs (470 nm) = b abs (470 nm) ff + b abs (470 nm) bb b abs (950 nm) = b abs (950 nm) ff + b abs (950 nm) bb BB(%) = where b abs (λ) is absorption coefficient, λ is wavelength, b abs (λ) ff a fossil fuel fraction and b abs (λ) bb a biomass burning fraction of absorption coefficient. Ångström exponents: α ff =1 for fossil fuel and α bb =2 for biomass.

Figure 2 .

 2 Figure 2. BC measurements during 2017-2021 in Orléans city.

  continuous observations over a period of nearly 5 years(mid 2017end 2021) in Orléans (central France) provides data to check to tendancy of the atmospheric BC concentration in the region for the last five years. Since only 25% of BC data was obtained in 2017 (FigureS1), we only used data from 2018 to 2021 to discuss the trend observed in this work. As shown in Figure4, a decrease of 3.6% per year is observed in the studied area. It has to be noted that a general decrease in the atmospheric BC has been reported in many other areas over Europe resulting mainly from the mitigation policies of air pollution in Europe since 1990's. For example, a decresae as high as 8±3 % per year has been reported by[START_REF] Singh | Trends of atmospheric black carbon concentration over the United Kingdom[END_REF] at Marylebone road in London.[START_REF] Sun | Decreasing trends of particle number and black carbon mass concentrations at 16 observational sites in Germany from 2009 to 2018[END_REF] reported a decreasing trend of BC concentration between -13.1 and -1.7 % per year from 2009 to 2018 over Germany.

Figure 2 .

 2 Figure 2. Four yeras trend of BC at the Voltaire supersite during the period 2018-2021.

  source during cold period. Another possible reason could be the boundary layer height decreased more rapidly to capture BC close to the earth surface in autumn and winter than that in spring and summer. The reduced human activities during the night hours would be the main reason for the observed lower BC concentration from the midnight until the morning rush hour in all seasons.

Figure 3 .

 3 Figure 3. Average diurnal variations of BC during different seasons for the period 2017-2021.

Figure 4 .

 4 Figure 4. Average diurnal profiles of BC, BC wb and BC ff during weekends and weekdays.

  Figure shows that BC concentration increased in the first two days from 27 to 29 March 2020 after the French government announced the first lockdown. This may be attributed to the increased traffic activities for residents to buy the necessary living supplies. Then the BC concentration kept at a low level during the first lockdown and after the lockdown. The second lockdown in France was less strict than the first one with about 30% of the population allowed to go to work. The high BC concentrations observed during the second lockdown was attributed, at least partly, to emission from household wood burning since the temperature was low during this period. Relatively high BC concentrations were also observed during Christmas Holidays period from 25 December 2020 to 15 January 2021 which also may be due to household wood burning. During the third lockdown covering the period 17 March to 3 May 2021, low

Figure 7 .

 7 Figure 7. Time series of BC, BC ff and BC wb concentration for the three lockdowns in France, (a) First Lockdown: before lockdown (6 March-26 March 2020), during lockdown (27 March-10 May 2020) and after lockdown (11 May-10 June 2020); (b) Second lockdown: before lockdown (29 September-28 October 2020), during lockdown (29 October-15 December 2020) and after lockdown (16 December 2020-16 January 2021); (c) Third lockdown: before lockdown (17 February-16 March 2021), during lockdown (17 March-3 May 2021) and after lockdown (4 May-3 June 2021).

Figure 8 .

 8 Figure 8. Column plots of average BC, BC ff and BC wb for the three lockdowns in France, (a) First Lockdown: before lockdown (6 March-26 March 2020), during lockdown (27 March-10 May 2020) and after lockdown (11 May-10 June 2020); (b) Second lockdown: before lockdown (29 September-28 October 2020), during lockdown (29 October-15 December 2020) and after lockdown (16 December 2020 -16 January 2021); (c) Third lockdown: before lockdown (17 February-16 March 2021), during lockdown (17 March-3 May 2021) and after lockdown (4 May-3 June 2021).

Figure 9 .

 9 Figure 9. Monthly averages of BC wb and BC ff in Orléans city

  Chaoyang Xue: Investigation, Formal analysis, Writing -review & editing. Ibrahim Ouchen: Investigation. Véronique Daële: Investigation, Writing -review & editing. Patrick Mercier: Investigation. Christophe Chalumeau: Investigation. Frédéric LE Fur: Investigation. Patrice Colin: Investigation. Abderrazak Yahyaoui: Investigation. Oliver Favez: Investigation, Writing -review & editing. Abdelwahid Mellouki: Supervision, Project administration, Writing -review & editing.

Table 1 .

 1 BC measurement in this work and compared with literatures. 188

	City	Study year	BC ± SD (μg m -3 )	Reference
	Barranquilla, Colombia	2018	16.1 ± 16.54	Blanco-Donado et al., (2022)
	São Paulo, Brazil	2017	8.5 ± 8.4 Weekday 5.2 ± 13.9 Weekday	Krecl et al., (2018)
	Macau, China	2016	4.0 ± 2.6 (morning) 3.1 ± 1.9 (afternoon)	B. Liu et al., (2019)
	Shanghai, China	2016	10.8 ± 3.5	M. Liu et al., (2019)
	Shanghai, China	2015	11.8 ± 9.8	Lei et al., (2017)
	Brisbane, Australia	2015	4.4 ± 7.3	Williams and Knibbs, (2016)
	Londrina, Brazil	2015	6.35 ± 20.0 (morning) 5.10 ± 14.7 (afternoon)	Targino et al., (2016)
			7.28 ± 1.63
	Shanghai, China	2014	9.43 ± 1.70	Li et al., (2015)
			8.62 ± 2.57
	Bogota, Colombia	2013	25.6 ± 39.2	Franco et al., (2016)
			2.5 ± 1.4 (morning)
	Minneapolis, USA	2012	0.7 ± 1.6 (afternoon)	Hankey and Marshall, (2015)
	Stockholm, Sweden	2011	2.4 ± 3.6	Krecl et al., (2014)
	Berkeley, USA	2011	1.76 ± 2.58 low traffic 2.06 ± 3.23 high traffic	Jarjour et al., (2013)
	Helsinki, Finland	2011	7.8 ± 4.3
	Rotterdam, Finland	2011	6.4 ± 3.3	Okokon et al., (2017)
	Thessaloniki, Finland	2011	10.9 ± 9.9
	Barcelona, Spain	2009	16.7	de Nazelle et al., (2012)
	Orléans, France	2017	0.75 ± 0.65	This work
	Orléans, France	2018	0.58 ± 0.44	This work
	Orléans, France	2019	0.54 ± 0.64	This work

Figure 1. Histograms of BC measurement data from 2017 to 2021.

3 2 1 0 2020/10/1 2020/10/11 2020/10/21 2020/10/31 2020/11/10 2020/11/20 2020/11/30 2020/12/10 2020/12/20 2020/12/30 2021/1/9 dat 4 3 2 1 0 2021/2/21 2021/3/3 2021/3/13 2021/3/23 2021/4/2 2021/4/12 2021/4/22 2021/5/2 2021/5/12 2021/5/22 2021/6/1

Acknowledgements

We thank le ministère en charge de l'environnement en France for the financement of mesures. C.X. thanks Cheng Wu (Jinan University) for providing the IGOR code for some data analysis shown in this study (Wu et al. ACP. 2018; https://zenodo.org/record/832396).

Funding

This work was supported by the National Natural Science Foundation of China (21976106) and funding of youth scientist from Research Center for Eco-Environmental Sciences -Chinese Academy of Sciences (RCEES-CAS). This study was also supported by the VOLTAIRE project (ANR-10-LABX-100-01) funded by the ANR and the PIVOTS project provided by the Region Centre -Val de Loire (ARD 2020 program and CPER 2015 -2020), and the Marie Skłodowska Curie Actions Programme (No. 690958) (MARSU).


Laurent Brisson
email: laurent.brisson@telecom-bretagne.eu

Jean-Claude Torrel
email: jtorrel@egiis.fr

Opinion mining on experience feedback: A case study on smartphones reviews

Through the development of electronic commerce, social media and collaborative media, the social commerce appeared. Social commerce, a subset of electronic commerce, is based on social interactions in order to buy and sell goods and services. Nowadays, before buying, people give more importance to the experience feedback they found on internet. However, it is difficult to get an overview of this experience feedback since it is scattered in many online resources, and buyers never have time to read many pages of comments. In this paper, we present an approach which grabs and analyzes experience feedback in order to publish a summary of opinions about a product. We develop this approach with a case study on smartphones and publish a dataset of thousands of comments on a wide range of smartphones. To summarize experience feedback, we use a linguistic appraisal model, based on appreciation, affect and judgement, and we set up an approach using methods and tools from the fields of natural language processing, opinion mining and sentiment analysis.

I. INTRODUCTION

Electronic commerce is a sector that is still growing rapidly today and have to face new challenges: the consumers are increasingly demanding as they look for the best value for their money and search lots of information on products they want. What has changed fundamentally is the nature of the consumer. Generations Y and Z behave differently with social media from previous generations. Now, social interactions have a strong and immediate impact on purchase behaviour. However, although it is now easy to find a huge amount of experience feedback on many goods and services, the only overall vision that one usually gets is a ranking. Thus, to form an opinion, it is often necessary to go through several pages of comments.

Experience feedback analysis consists in analyzing consumers' comments which may contain emotions and explicit or implicit facts. To deal with such data, while the amount is far beyond the reading capability of a human being, automatic methods to extract and summarize opinions, known as opinionmining or sentiment analysis, emerged. Opinion mining is the field that deals with the analysis of subjective statements from texts, the identification of opinions, the estimation of their orientation and the extraction of arguments that relate to these opinions. Opinion mining can be performed at various levels of analysis: document level, sentence level and aspect level. Document level opinion mining analysis [START_REF] Blitzer | Biographies, bollywood, boomboxes and blenders: Domain adaptation for sentiment classification[END_REF] is beyond the scope of this paper since it aims to give an overall rating of the polarity of a document. Sentence level opinion mining analysis [START_REF] Farra | Sentence-level and document-level sentiment mining for arabic texts[END_REF], [START_REF] Sharma | Polarity detection at sentence level[END_REF] is well-fitted to polarity classification of short texts like tweets or short reviews. However, that kind of analysis does not consider the various aspects of an opinion, that is why lots of works mainly focus on aspect level polarity. Aspect level analysis is often a two-step task: the first one consists in aspect extraction while the second one focuses on polarity detection which requires opinion word extraction.

In this paper, we address a specific challenge related to aspect level opinion mining, building a platform which summarizes and qualifies experience feedback from reviews. As a constraint, these reviews may be from various domains and the whole analysis process must be deployed in a short period. This constraint excludes the use of supervised learning algorithms that often need lots of works to create a relevant learning dataset. The novelty of this approach and its application, relies on the use of a linguistic framework which focuses on appraisal in English [START_REF] White | The Language of Evaluation: Appraisal in English[END_REF] and a process that implements this framework in two main steps: the first one assists an expert to quickly create a knowledge base in order to extract relevant opinions and the second one runs syntactic analysis and search strategies. As part of our contribution, we make our dataset and a validation set available to the community in order to allow cross-validation with other approaches [START_REF] Brisson | Smartphones dataset[END_REF]. This paper is organized as follows. Section 2 presents related work in the field of opinion mining and sentiment analysis. Section 3 presents the linguistic framework we use. Section 4 presents the overall approach, methods and tools. Section 5 presents appreciation and affect extraction methods. Section 6, presents the platform, the results and discusses future work.

II. OPINION MINING AND SENTIMENT ANALYSIS

Aspect extraction approaches can be divided into unsupervised approaches, mainly domain and language independent, and supervised approaches that often require manually labeled data to train models. Unsupervised approaches include statistical and rule-based approaches. Hu and Liu [START_REF] Hu | Mining opinion features in customer reviews[END_REF] apply apriori algorithm to find frequent aspects. Firstly, they apply a Part-of-Speech (POS) tagging algorithm in order to split the text into qualified words (nouns, verbs, adjectives, etc.). Then, after association rules extraction, the most relevant aspects are extracted by pruning the most frequent itemsets which are meaningless or redundant. Using some linguistic rules that correlate adjectives with opinion expression [START_REF] Hatzivassiloglou | Effects of adjective orientation and gradability on sentence subjectivity[END_REF], they extract opinion words and evaluate their orientation. Popescu and Etzioni [START_REF] Popescu | Extracting product features and opinions from reviews[END_REF] achieve higher precision using relaxation labeling, an unsupervised classification algorithm, which takes into account the context of a word and some local constraints manually specified (conjunctions, disjunctions, syntactic dependency rule templates, morphological relationships, synonyms and antonyms). Supervised approaches, also called "model-based approaches", aim to improve statistical based approaches with some features -in the machine learning sense -that cannot be detected without training. Kessler and Nicolov [START_REF] Kessler | Targeting sentiment expressions through supervised ranking of linguistic configurations[END_REF] use Support Vector Machine and manually labeled data to train a model that ranks extracted aspect/opinion pairs. Moghaddam [START_REF] Moghaddam | Opinion digger: an unsupervised opinion miner from unstructured product reviews[END_REF] uses Generalized Sequential Pattern (GSP) mining to find patterns to extract relevant opinions. Zheng and al. [START_REF] Zheng | Incorporating appraisal expression patterns into topic modeling for aspect and sentiment word identification[END_REF] uses a variant of Latent Dirichlet Allocation for jointly extract aspect and sentiment words.

Several sources of knowledge can be used to detect polarity: corpus, dictionaries and lexicons. Corpus-based approaches try to find co-occurrences of words or phrases to detect opinions. Dictionary-based approaches use synonyms, antonyms and generally WordNet dictionary [START_REF] Miller | Wordnet: A lexical database for english[END_REF]. Lexicon-based approaches use lexicons like SentiWordNet [START_REF] Esuli | Sentiwordnet: A publicly available lexical resource for opinion mining[END_REF] or create their own lexicon. In the field of lexicon induction, some works focus on unsupervised induction based on subjective features [START_REF] Turney | Thumbs up or thumbs down?: Semantic orientation applied to unsupervised classification of reviews[END_REF], while others use labeled data [START_REF] Miao | Mining fine grained opinions by using probabilistic models and domain knowledge[END_REF]. Blair-Goldensohn et al. [START_REF] Blair-Goldensohn | Building a sentiment summarizer for local service reviews[END_REF] adopt a semi-supervised approach and use provided labels and domain specific characteristics of service reviews to perform polarity classification. They implement a common approach using a set of seed words (words whose polarity is already known) to infer opinions [START_REF] Andreevskaia | Mining wordnet for a fuzzy sentiment: Sentiment tag extraction from wordnet glosses[END_REF]. Some approaches do not consider aspect-level analysis as a two-steps task and perform a joint aspect/opinion extraction. Miao et al. [START_REF] Miao | Mining fine grained opinions by using probabilistic models and domain knowledge[END_REF] and Li et al. [START_REF] Li | Structure-aware review mining and summarization[END_REF] merge aspect and opinion extraction in a unified process using Conditional Random Fields (CRF). CRFs models are conditional probabilistic sequence models like Maximum Entropy Markov Models (MEMM).

III. "WAYS OF FEELINGS": LINGUISTIC FRAMEWORK AND SEMANTIC RESOURCES

A. A linguistic framework for appraisal

People give their feedback about goods and services in many different ways. One can write a well-structured review and be factual while others are subjective and express their feelings. Natural Language Processing (NLP), opinion mining and sentiment analysis fields provide lots of methods and algorithms to analyze, classify and evaluate the polarity of various kind of documents.

To structure and to conduct our computational processes a framework modeling the language seemed us necessary. Since we have to deal with experience feedback, we choose to use the framework proposed by two linguists, James R. Martin and Peter R. R. White [START_REF] White | The Language of Evaluation: Appraisal in English[END_REF] which focuses on appraisal in english. Appraisal is a discourse semantic resource which includes attitude (dealing with our feelings), engagement (dealing with the way to express feelings) and graduation (dealing with the way to intensify or to soften feelings). To achieve our goal we decided, in a first approach, to focus on attitude. In their framework, Martin and White, define attitude with three concepts: appreciation, affect and judgement.

Definition 1 (Appreciation): Appreciation involves evaluations of semiotic and natural phenomena, according to the ways in which they are valued or not in a given field. [START_REF] White | The Language of Evaluation: Appraisal in English[END_REF] Our appreciation extraction method is an aspect-based opinion mining task. We aim to extract three different expressions of appreciation: explicit qualifier about an explicit aspect "The battery life is good", "The only problem is the camera" (here the qualifier could be a noun (NN) or an adjective (JJ)), the existence (or not) of an aspect "It doesn't have auto-focus" and the behavior of an aspect "Touch screen lags a bit at times after an app is closed". Definition 2 (Affect): Affect is concerned with registering positive and negative feelings: do we feel happy or sad, confident or anxious, interested or bored ? [START_REF] White | The Language of Evaluation: Appraisal in English[END_REF] Our affect extraction method aims to extract two different expressions of affect: affect as a "mental processes" "I am enjoying with this phone", "I hate this phone", "I like its display" and affect as a "quality" "I'm very happy". Consequently we perform an aspect-based opinion mining task. and a sentiment analysis of the sentence. Definition 3 (Judgement): Judgment deals with attitudes towards behavior, which we admire or criticize, praise or condemn. [START_REF] White | The Language of Evaluation: Appraisal in English[END_REF] According to Martin and White, judgement is clearly different from appreciation in the way judgement express feelings about behavior while appreciation express feelings about the value of things. In our context, handling judgement is out of scope but it's a perspective for future work. That is why, in the following, we consider opinion mining from experience feedback as two tasks: appreciation extraction and affect extraction.

N-gram Analysis on technical specifications N-gram Semantic Extension Wordnet

N-gram Analysis on reviews Intersection of the n-gram sets

Human validation Knowledge base of relevant aspects and behaviors

Fig. 1. Knowledge base building process

B. A knowledge base for semantic relevance

The knowledge base is an important part of our approach since it allows us to define a relevant semantic perimeter for the analysis. However, as one of our constraints is to be able to deploy the whole process of our approach in a short period we have to minimize interactions with an expert to validate the content of this knowledge base. The knowledge base is composed of two resources: an aspect-lexicon which is a list of words and phrases defining the relevant aspects for an opinion mining task and a polarity lexicon (we use Sentic lexicon [START_REF] Cambria | Senticnet: A publicly available semantic resource for opinion mining[END_REF]) which gives us polarity information about words. In the next section we explain how this information help us to extract relevant opinions.

Before analyzing opinions, we have to collect textual resources of the same domain in order to help the expert to build the aspect-lexicon. For example, in our experiment we gathered technical specifications about various smartphones. Then, we performed 1-gram and 2-gram analysis on both technical specifications dataset and user comments dataset. We restricted 1-gram to words which are 'nouns' in their sentence. Then, the 1-gram and 2-gram sets from technical specifications were extended with Wordnet [START_REF] Miller | Wordnet: A lexical database for english[END_REF] using synonyms, direct hyperonyms and direct hyponyms and we intersected n-gram list from reviews with the extented n-gram list from specifications in order to select relevant aspects. In the last step, an expert validated the list. Here are some recommendations to the expert to quickly select the most relevant terms. First, check the most frequent words which are present in both dataset (for example, in our experiment we considered only words occurring more than 100 times). Second, filter trademarks and proper nouns: these words could be used later for automated context definition. Last, check words added with synonymy, hypernonymy or hyponymy relationships since these word are most likely the less relevant ones.

IV. APPRAISAL PATTERNS EXTRACTION

A. Overall approach

Figure 2 gives an overwiew of our approach. First, we perform lexical analysis and lexical transformations on our dataset: character cleaning, full stop detection, tokenization and part-of-speech tagging. Second, we perform syntactic analysis. For these two first tasks we use OpenNLP [START_REF] Kottmann | Apache opennlp. the apache software foundation[END_REF]. Finally, we use a custom knowledge base, in order to extract relevant speech elements related to appreciation or affect in the context of smartphone reviews. This approach is based on two key concepts: syntactic analysis and strategies to find appraisal patterns in parse trees. Syntactic analysis is performed using constituency-based parse tree which are based on Chomsky language theory [START_REF] Chomsky | Knowledge of Language: Its Nature, Origin, and Use, ser. Convergence[END_REF] and use constituent grammars. A constituency-based parse tree (Figure 4) focuses on the linear structure of the phrase where intermediate nodes are non-terminal grammatical categories and leaves are terminal grammatical categories [START_REF] Santorini | Part-of-speech tagging guidelines for the penn treebank project (3rd revision)[END_REF]. Our strategies, to find appraisal patterns, follow three steps: a breadth-first search (BFS) in the parse tree, pattern recognition using automata and rule triggering to extract relevant opinions. During the breadth-first search in the parse tree, each node is submitted to an automaton to match some patterns. We made the choice to use automata because they are a sound and readable method for pattern extraction, but they are not in the core of our approach and could be replaced by another suitable method. When a node of the tree triggers a transition, its sub-trees are handled by the current state of the automaton. When an automaton reaches a final state, a rule is triggered

Reviews dataset Aspect Lexicon

Sentic Polarity Lexicon

Lexical Analysis and Transformations

Syntactic Analysis

Set of parse trees for each sentence

Search strategies implementing appreciation and affect extraction

Opinions and emotions

Knowledge Base

Fig. 2. Overview of the approach according to some conditions. These conditions are mainly about word polarity or word presence in the knowledge base. They are explained in the next section. For this purpose, all nodes triggering a transition are saved into a variable. Let N be the set of all nodes which trigger a transition toward the state S of an automaton. We define V s the saved variable for S state, as the union of all leaves of the nodes of N . Properties of saved nodes are: polarity (given by Sentic lexicon [START_REF] Cambria | Senticnet: A publicly available semantic resource for opinion mining[END_REF]), chunk or part-of-speech tag, and membership to our custom aspect knowledge base kb. In our approach, polarity inverter words such as 'not' or 'no' are called modifiers.

B. Appreciation Extraction

Appreciation extraction consists in several strategies to extract aspect qualification, aspect presence and behaviour qualification.

RB

DT JJ α 1 α 2 α 3 JJ NN RB JJ Fig. 3. α automaton is dedicated to extract explicit adjectives on an aspect 1) Aspect qualification: We consider two ways to qualify an aspect: using adjectives and using nouns. The α automaton (Figure 3) is designed to extract explicit adjectives about an aspect. For example, let's consider the sentence "What a good phone!" (Figure 4). We define a rule which is triggered if α3 is in the aspect-lexicon and if α2 is a polarized adjective. That rule extracts modifiers from α1 (none in this example since there isn't a negative word in the sentence), qualifiers from α2 ('good') and the aspect from α3 ('phone'). In order to handle more complex syntactic structures we define the γ automaton (Figure 6). It is a hierarchical automaton that handle several rules related to appreciation but also to affect. Sub-automata γB and γC are recursives: all the states of γB trigger γB with a 'VP' node and all the states of γC trigger γC with a 'NP' or 'PP' node. This property allows us to handle the conjunction 'and' since it creates nested 'NP' phrases. Negation words are handled with γ3 and γ4 states within each rules presented below.

Another way to qualify an aspect is to use nouns instead of adjectives. To understand how we extract nouns that qualify an aspect let's consider the sentence "The camera is the only problem" (Figure 5). A rule is triggered if γ1 is in the aspectlexicon of the knowledge base and if γ6 is a polarized noun that is not in the knowledge base. Thus, we extract the aspect 'camera' from γ1 (since the word is in the aspect-lexicon) and the qualifier 'problem' from γ6 since this word has a negative polarity and is not in aspect-lexicon. 2) Aspect presence: Let's consider the sentence "The phone has a good camera" (Figure 7). In order to detect the presence of an aspect we use γ automaton and we define a rule that is triggered if γ1 and γ6 are in the aspect-lexicon and if γ3 contains a verb expressing membership. Thus, we extract the aspect from γ1 ('phone') and the qualifier from γ6 ('camera').

3) Behaviour qualification: In order to detect an opinion about the behaviour of an aspect we use γ automaton and we define a rule that is triggered if γ1 is in the aspect-lexicon and γ3 contains a polarized verb. Thus, we extract the aspect

C. Affect Extraction

In order to extract affect expressed like a mental process we use γ automaton and we define a rule that is triggered if γ1 is the personal pronoun 'I', γ6 is in the aspect-lexicon and γ3 is a polarized verb. Let's consider the sentence "I hate this phone" (Figure 9). Since the personal pronoun 'I' and the polarized verb 'hate' are in the sentence, we extract the aspect from γ6 ('phone') and qualifiers from γ3 ('hate').

A. Datasets

We built a dataset about 368 smartphones, containing 40,160 comments and 81,431 sentences for a total size of 8.5 Mo. We also gathered their technical specifications from 8 manufacturer websites. In order to validate our approach we selected a subset of our dataset and we gave the task to 3 people to read and annotate 527 comments (containing 708 opinions) on 6 phones. These people had to find qualifiers about smartphones aspects and to qualify their polarity. We only keep, in the final validation dataset, annotations with a consensus between the three reviewers. These datasets are available online [START_REF] Brisson | Smartphones dataset[END_REF].

B. Results

We assess the performance of our approach considering two tasks: finding relevant appreciation in user feedback and qualifying the polarity of an appreciation. For this evaluation, we use the three following measures: precision, recall and fmeasure.

Dataset

Appreciation show performances of our approach over the validation dataset. Average precision to find relevant appreciation is 0.87% and average precision to qualify polarity is 0.81%. Polarity errors are mainly due to semantic confusions since we do not consider the context of the sentence to define the polarity of a word. Average recall to find relevant appreciation is 0.57% and average recall to qualify polarity is 0.55%. These results satisfy our objective to extract relevant comments to understand the pros and cons of each smartphone, however, the recall of our approach have to be improved. There are several issues to explain this low recall that we plan to address in our future work. First, we did not yet implement affect extraction expressed like a mental state. Furthermore, it is difficult to handle complex sentences with constituencybased syntactic analysis. Finally, we do not link several phrases in the same sentence. For example, in the sentence "I have just bought this phone, it lags!", we currently do not consider 'it' as the 'phone' and our behavior qualification rule is not triggered.

C. Discussion and future work

These results show we address our initial challenge to extract relevant opinions without supervised learning on a specific dataset. In order to improve the number of opinions detected, we want to consider two possible approaches: the syntactic one and the pragmatic one. We can improve syntactic analysis with dependency-based syntactic analysis [START_REF] Marneffe | Generating typed dependency parses from phrase structure parses[END_REF], [START_REF] De Marneffe | The stanford typed dependencies representation[END_REF] which provides grammatical relationships between words to handle more complex phrases. Since, interpreting grammatical dependencies is a difficult task, we plan to bootstrap this work using our current approach. The pragmatic approach is to consider contextual information during semantic analysis. This context can be handled within a document or outside a document. To handle context within a document, we plan to build a context-based polarity lexicon. For this purpose, we have to transform our knowledge base into an ontology to be able to manage several semantic domains. To handle context outside a document, we plan to collect data on the use of the platform we deployed in order to gather user feedback about our opinion summaries.

VI. CONCLUSION

Social commerce is the new challenge of electronic commerce. Nowadays, before buying, people give more importance to the experience feedback they found on internet than to products' technical specifications. However, although it is now easy to find a huge amount of experience feedback on many goods and services, it is often necessary to go through several pages of comments to have a coherent overview. In this paper, we present a platform that summarizes and qualifies userexperience feedback. We use a sound language framework modeling appraisal in English and two core concepts to model attitude: appreciation and affect. In a first step, we built a knowledge base in a interactive way with a expert in order to define a relevant semantic perimeter for the analysis. Then, we applied a constituency-based syntactic analysis and strategies to find appraisal pattern in parse trees. These parse trees were analyzed using automata and rules in order to extract appreciation and affect expressions. We built two datasets which are available online [START_REF] Brisson | Smartphones dataset[END_REF] to allow cross-validation with other approaches, the first one is a full dataset with 368 smartphones, while the second is a validation dataset, annotated manually to evaluate and compare approaches. Our results show good precision performances which guarantees relevant opinion summaries on the platform.

Fig. 4 .

 4 Fig. 4. Parse Tree of "What a good phone!"

Fig. 5 .

 5 Fig. 5. Parse Tree of "The camera is the only problem"

Fig. 6 .Fig. 7 .Fig. 8 .

 678 Fig. 6.γ automaton is hierarchical: it triggers γB and γC which are recursives. A dashed loop means each state of the automaton can trigger the automaton itself (see section IV-B1).

Fig. 9 .

 9 Fig. 9. Parse Tree of "I hate this phone"

Fig. 11 .

 11 Fig. 11. Polarity evaluation performances