Stéphane Calvez
email: scalvez@laas.fr

Oleksandr Stepanenko

Kevin Louarn

Emmanuelle Daran

Alexandre Arnoult

Guilhem Almuneau

Stephane Calvez

Selective wet oxidation of AlAsSb alloys on GaAs

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

Introduction

The oxidation of aluminum-containing III-V semiconductors is a technological process primarily used to introduce optical and/or electrical confinements in opto-electronic devices including Vertical-Cavity Surface-Emitting Lasers 1,[START_REF] Dallesasse | Proceedings of the IEEE[END_REF] , passive and active waveguide-based devices such as edge-emitting lasers [START_REF] Dallesasse | [END_REF] , whispering-gallery-mode micro-resonators 4 , photonic crystal 5 and nonlinear 6,7 waveguides. To-date, most of the reported material studies have been dedicated to the oxidation of AlGaAs compounds. They have provided a comprehensive dataset on its kinetics [START_REF] Dallesasse | [END_REF][8][START_REF] Choquette | Selected Topics in Quantum Electronics[END_REF][10] , on its compositional and structural selectivity [11][12][13][14] , on its anisotropy [15][16][17] as well as on the structural properties of the resulting oxide [18][19][20] . Analysis of these data has revealed that this type of oxidation is a complex interplay between localized chemical reactions and the diffusion of the reactants and by-products through the oxide [21][22][23] , the latter two mechanisms being further compounded by strain effects [24][25][26][27] arising from the volume shrinkage occurring during the transformation of the semiconductor into its corresponding oxide. Investigations of the oxidation of other Al-containing III-V semiconductors have also been carried out to gain additional chemico-physical insight and to potentially expand the use of this process to make transistors or photonics devices operating in other spectral bands than the near-infrared window (800-1000 nm). So far, the studied alloys include AlIn(Ga)P 28 on GaAs, AlInAs [29][30][31][32] and AlAsSb [33][34][35] on InP, AlInN on GaN 36,37 , and AlAsSb on GaSb [38][39][40][41][42] .

In this paper, we report on the oxidation of AlAsSb compounds quasi-latticed-matched to GaAs with a view to provide experimental information on the influence on the process of alloying (and bi-axially straining) the to-be-oxidized layer with antimony rather than with gallium. This study complements the data presented in reference 43 as it focusses on samples with low Sb-content to limit the amount of antimony segregation 44 .

Sample and process description

The samples used in this study consist of a 5-pair 50-nm GaAs/50-nm AlAs superlattice used to calibrate the growth rates and that is subsequently buried under 1 µm of GaAs, a 50-nm thick AlAs1-xSbx layer capped with 500-nm thick of GaAs.

The samples were grown by molecular beam epitaxy on (001)-oriented GaAs substrates using standard conditions for AlGaAs layers. The AlAsSb layers were grown using a V/III ratio of 3.2 and temperature of 580°C. We point out that the incorporation of the Sb into the AlAsSb layer depends nonlinearly on the flux of Sb4 supplied by the valved cracker effusion cell. The composition and thickness of the AlAs1-xSbx layers were determined using fits of the High-Resolution X-Ray Diffraction (HR-XRD) measurements made around the GaAs (004) reflection peak and displayed in Fig. 1. The HR-XRD curves show a good structural quality of the grown AlAsSb layers with no indication of relaxation regardless the Sb composition. Narrow small satellites peaks on Fig. 1 originates from the underlying superlattice. Sample processing started by defining 42-µm-side-square and 42-µm-diameter-circular mesas by contact lithography into SPR700 photoresist. These patterns were subsequently transferred into the III-V semiconductor multilayer using an isotropic wet etching solution (H3PO4/H2O2/DI water with 3/1/25 dilution ratio). The ~800-nm-deep etched sidewalls of these mesas give access to the AlAsSb layer for lateral oxidation upon resist removal. The wet oxidation was performed straightaway in a vacuum furnace commercialized by AlOxtec that provides uniform, controlled, and reproducible treatment of up-to-4-inch wafers. Its reduced pressure (500 mbar unless stated otherwise) chamber is supplied with an oxidizing atmosphere composed of water vapor (10 g/l) and N2/H2 (95%/5%) forming gas supplied at a flow rate of 0.6 l/min.

Measurement methods of the oxide aperture shape and oxidation-related volume change

The oxidation furnace is equipped with an in-situ micro-reflectometry-based imaging system 45 that is used to acquire the real-time monitoring data presented and analyzed hereafter. Typical top-view images recorded during the lateral oxidation of an AlAs, AlAs0.967Sb0.033 or AlAs0.866Sb0.134 layer from circular etched mesas are presented in Fig. 2. On these images, the edge of the etched mesas is black and the so-called oxide apertures are the boundaries between the central light grey and the white areas that corresponds respectively to the (unoxidized) semiconductor and oxide zones. As it can be observed in Fig. 2, the oxide aperture shape often differs from a scaled version of the mesa contour, the deformation being caused mostly by an anisotropy occurring during the oxidation process and by diffusion-based smoothing effects. The above-mentioned anisotropy can originate from a crystallographic dependence of the reactions 15,23 , from an anisotropic diffusion of the reactants or of the by-products through the formed oxide, or from an uneven distribution of the oxide-induced strain field 26 . In this particular study, except for the AlAs0.866Sb0.134 layer and as already observed for AlGaAs-layers on (001)-oriented substrates, the lateral oxidation of an AlAsSb layer from a circular (or a square) mesa leads to an oxide aperture whose shape can be described as a linear combination of a circle and a square 17,46,47 . The square fraction, s, (ranging from 1 for a square aperture to 0 for a circular aperture) can then be used as a quantifying parameter of the degree of oxidation anisotropy 23,46 .

For the richest-Sb samples (AlAs0.87Sb0.13), the observed shapes of the oxide apertures on the images of the bottom line of Fig. 2 suggests that the oxidation rates along the (110) and the (1-10) directions are no longer identical. These deformed contours thus require two anisotropy parameters to capture their shape in full and could be, in practice, effectively fitted using a linear combination between an ellipse and a rectangle. Dark vertical lines on the (white background) oxide can also be noticed at all displayed oxidation times and their presence is related to Sb segregation as it will be discussed later.

Besides the oxidation kinetics and anisotropy, another important factor to monitor is the strain given that these parameters are interlinked 26 . This is of specific relevance to this study since, as the Sb content in the AlAsSb layer increases, the strain field (initial) distribution is progressively modified because of the (in-wafer-plane) bi-axial compression of the AlAsSb layer that is imposed during the epitaxy of this material whose bulk lattice constant presents relative mismatch with the GaAs substrate ranging from 0.1% ([Sb]=0%) to 1.2% ([Sb]=13.4%). Furthermore, the oxidation process itself is known to be associated with a material volume modification that induces a (dynamic) variation of the strain field distribution. In particular, it has been established that the oxidation of AlAs layers leads to a volume contraction (usually characterized by a ~12% thickness reduction 18 ) whereas the oxidation of AlAs0.08Sb0.92 layers comes with a large volume expansion (~150% thickness increase 42 ). The variation of AlAsSb composition is therefore likely to lead to pronounced changes in the oxidation that are strain related. Among the various methods available to characterize the strain distribution in oxidized AlGaAs mesas 24,48,49,26,27,50 , we here chose to indirectly quantify the strain by simply measuring the aperture-averaged height difference at the top surface between the (unoxidized) semiconductor and the oxide. Technically, this parameter is extracted from postoxidation atomic force microscope surface profiles that are acquired in tapping mode at room temperature over ~20µm-wide square regions centered around ~8µm oxide apertures (see Fig. 3). It is worth pointing out at this stage that the presence of the capping layer separating the oxidized layer from the surface might lead to underestimate the embedded volume contraction.

Kinetic and parametric study of the oxidation of AlAsSb

Behaviour of AlAs oxidation

To start with, oxidations of reference (AlAs-containing) samples were performed at different temperatures, sampling the standard range between 380 and 440°C. Typical examples of the extracted temporal evolutions of the aperture inner dimension and square fraction are presented in Fig. 4. Irrespective of the shape (circular or square) of the etched mesa, the oxidation progression is observed to be linear with a marginal acceleration just before aperture closure. This linear kinetics suggests that the oxidations are limited by the reactions taking place at the aperture and not by the reactant/by-products diffusion through the oxide. As illustrated in Fig. 2 and already reported for instance in 17,47 , the aperture shape deforms throughout the oxidation duration. For circular etched mesas, the oxidation is observed to occur at rate very close to the fast-axis (along the (100) direction) value (see Fig. 4 and23 ). Moreover, as the oxidation progresses, the square fraction is seen to increase up to a maximum value that is reached when the aperture inner dimension gets to the size (12 µm here) below which the observation system optical resolution restricts the ability to discriminate squares from circles. As established in 17,23 , the (circular-etchedmesa) oxidation rate and the maximum square fraction constitute a sufficient basis to fully represent (four-fold-symmetric) oxidations and will therefore serve as quantifying parameters for the hereafter analysis of the oxidation of low-Sb-containing AlAsSb layers. However, full data analysis allowed to establish that the ratio of the oxidation rate along the (100) direction, V(100), to the one along the (110) direction, V(110), can be empirically related to the maximum square fraction, smax, using V(100)/V(110)~0.74*(smax-0.14)+1 .

Influence of temperature

The temperature dependence of the oxidation of AlAsSb layers is illustrated on Fig. 5 for two Sb compositions. As commonly observed, the oxidation rate increases with temperature. Since the process involves chemical reactions, the oxidation temperature dependence is often analyzed 9,10 using an Arrhenius law given by 𝑉 = 𝑉 • 𝑒 / Eq. 1

where kB is the Boltzmann constant, Ea is the activation energy

The extracted activation energies, plotted in Fig. 6, are seen to reach a minimum for a Sb concentration of 42%. This trend is similar to the one reported in 43 except for a shift (from 134% to 42%) in the concentration of the minimal activation energy that may originate from differences in the experimental conditions 10 (in particular in the oxidation pressure and sample preparation). As for the anisotropy, it can clearly be observed (on the central subplot of Fig. 5) that, as in the case of Al0.98Ga0.02As layer oxidation 17 , the process becomes more isotropic as the temperature increases. In the studied range of concentrations, the rise in the oxidation temperature is also found to be associated with an increase in strain, in opposition to the evolution of the anisotropy. It is however worth noting that the measured values of the semiconductor-to-oxide height step suggest a much lower oxidation shrinkage than the established value (for AlAs 18 ). This artefact may be caused by the fact that the (500-nm-)thick GaAs cap separating the sample surface profile from the top oxide-interface profile may relieve part of the strain, leading to smoother recorded profiles.

Influence of Sb composition

The analysis of the data in terms of compositional dependence is presented on Fig. 7 for compositions where four-fold symmetry oxidation is maintained (all except [Sb]=13.4%). First and foremost, the addition of Sb is found to slow the oxidation kinetics for all the studied compositions and temperatures, thereby suggesting that the reaction involving antimony is the least thermodynamically favorable of the whole reaction chain in this range of process temperatures. Furthermore, a minimum in oxidation rate is achieved in the ~2-6% Sb content range. Alloying AlAs with antimony is nevertheless observed to be more favorable than introducing gallium to maintain fast oxidation kinetics. Indeed, the oxidation of AlAs1-xSbx (x>0) compounds is, in the worst case, two times slower than the oxidation (in the same conditions) of a layer (of the same thickness)

of AlAs taken as reference material while the conventionally-used Al0.98Ga0.02As compound leads to oxidation rates that are at least 3 times slower [START_REF] Choquette | Selected Topics in Quantum Electronics[END_REF] than the AlAs reference. The anisotropy is seen to follow the same trend as the oxidation rate (see Fig. 7 and Fig. 8 and additional point for [Sb]=13.4% in deduced from Fig. 11). In contrast to what has been observed for the temperature dependence, the strain here follows the same evolution as the anisotropy. This difference in behavior might be explained by the fact that the (measured) strain combines two contributions: one originating from chemically-induced volume change and the second one building up during the sample cool-down phase (from the process temperature) because of the difference in the materials thermal expansion coefficients. The results of Fig. 7 being recorded at fixed process temperatures, the measured strain evolution, derived from the AFM maps shown in Fig. 8, is thus solely representative of the variation in the reaction-induced strain. Assuming that, for a fixed process temperature, there is a linear relationship between the measured semiconductor-to-oxide step height and the volume contraction occurring upon oxidation, the inferred volume shrinkage of AlAs0.967Sb0.033 is therefore of ~7 %, a value similar to the one achieved upon oxidation of Al1-xGaxAs layers with x in the 2 to 8% range 18 .

Influence of the oxidation chamber pressure

Since the oxidation chamber pressure modifies the supplied water concentration 51 and can also change the thermodynamics of the chemical reactions and their sequence, this study is completed by investigating the influence of this process parameter on the oxidation characteristics of AlAs0.967Sb0.033 layers. The results displayed on Fig. 9 reveal that the oxidation rate increases with pressure according to a saturating law (in blue) given by: V= Vsat P/(P+Psat) Eq. 2 with a saturated progression rate of Vsat=0.77 µm/min and a saturation pressure of Psat=50 mbar.

Once again, the anisotropy and the strain parameters are found to follow the same behavior, both being maximum for a chamber pressure of 100 mbar and decreasing on either side. The latter observation suggests that water-supply-limited processes lead to lower volume contraction and, in turn, to more isotropic progression.

High-Sb-content study

Finally, when the Sb concentration becomes too large ([Sb]=13.4% here), Sb segregation occurs as already observed in 43,44 .

Experimentally, this phenomenon appears on the micro-reflectivity images with droplets emerging at the oxide aperture and subsequently migrating towards the etched mesa contour thereby creating (dark vertical) channels in the (white background) oxide region (see Fig. 2). This effect is believed to be strain-driven since the droplets arise at the location of maximum strain 27 and also because the resulting Sb removal channels are preferentially aligned with the strain-prone (110)-orientation and lead, upon aperture closure, to the drumhead patterns of Fig. 10 that look similar to the ones occasionally induced by strain during the oxidation in AlGaAs mesas 25 . This statement is further backed-up by the measured AFM surface profiles that show that the Sb-removal channels are ~20-nm-high, much taller than the ~2.2 nm semiconductor/oxide step measured along the (1-10) direction (see Fig. 11). Finally, it also worth noting that the appearance of these Sb removal channels significantly modify the oxidation rate (polar) diagram, lifting the degeneracy between the (110) and (1-10) directions, the former becoming the fastest oxidation direction with a rate ratio of ~1.26 through a combination of strain enhancement and more effective Sb removal.

Conclusion

In this paper, the oxidation of AlAsSb nearly lattice-matched to GaAs, with several Sb concentrations up to 13.4%, has been studied in detail to gain further insight in the mechanisms ruling the influence of small amounts of Sb on the AlAs oxidation process. In particular, it was established that for Sb content smaller than 7%, the AlAsSb layers can be oxidized without Sb segregation at a slower rate than their AlAs counterpart, indicating that Sb plays a similar role as Ga in AlGaAs oxidation but with a less drastic impact on the resulting kinetics. The investigations have also shown that oxidizing AlAs0.967Sb0.033 layers at low temperature (380°C) and in water-restricted supply (low and high pressure) leads to less-strain-baring samples and more isotropic processes, properties which can only be achieved at much higher temperatures (~480°C) with the commonly used Al0.98Ga0.02As compounds. The above-mentioned characteristics make thin AlAsSb layers new and attractive alternatives to their AlGaAs counterparts in the fabrication of oxide-confined opto-electronics devices such as VCSELs. Additionally, the study of the oxidation of AlAs0.866Sb0.134 layers has revealed the critical role played by strain in the emergence of the detrimental Sb segregation.

Fig. 1 .

 1 Fig. 1. High-Resolution X-Ray Diffraction theta-2*theta scans of the as-grown samples, the angular reference being the (004) substrate reflection peak. The curves are deliberately shifted along the vertical axis by multiplication by a factor 100 for enhanced visibility.

Fig. 2 .

 2 Fig. 2. Infrared microscopy images measured in-situ during the oxidation (at 420°C, 500 mbar) of 42µm-diameter circular mesas of AlAsSb with different Sb concentration.

Fig. 3 .

 3 Fig. 3. Infrared image (left) of a 52µm-diameter circular mesa embedding an AlAsSb6.9% layer oxidized at 380°C -500 mbar for 63min and its atomic force microscope surface profile (right).

Fig. 4 .

 4 Fig.4. Time dependence of the oxidation (420°C -500 mbar) of AlAs from 42µm etched mesa whose shape is a circle (red), a (110)-oriented (blue) square or a (100)-oriented (green) square. The start time t=0min is taken here as the time when the aperture of the (110)-oriented square can be distinguished from its etched mesa contour.

Fig. 5 .

 5 Fig. 5. Temperature dependence of AlAs (blue) AlAs0.967Sb0.033 (pink) oxidation: rate, square fraction of the aperture shape and semiconductor-to-oxide step height measured from circular mesas. Measurement error bars are indicated when relevant.

Fig. 6 .

 6 Fig. 6. Composition dependence of the oxidation rate activation energies along (110) and (100) orientations with associated error bars.

Fig. 7 .

 7 Fig. 7. Composition dependence of the oxidation characteristics measured for oxidations performed at 380 (blue) and 420°C (red). Near-infrared images with oxide extent of ~10 µm are shown as inset to give visual illustration of the Sb-content influence on the aperture anisotropy.

Fig. 8 .

 8 Fig.8. AFM surface maps used to derive the oxidation strain evolution with Sb-content which is presented in the lower subplot of Fig.7(when the oxidations were performed at 420°C).

Fig. 9 .

 9 Fig. 9. Pressure-dependence of the oxidation of AlAsSb3.3% layers. The curve in blue is the fitted saturating law of Eq. 2. Near-infrared images with oxide extent of ~10 µm are shown as inset to give visual illustration of the Sb-content influence on the aperture anisotropy.

Fig. 10 .

 10 Fig. 10. Typical strain-induced drumhead pattern observed upon full oxidation (at 420°C & 500 mbar for 20 min) of AlAsSb13.4%-containing 52-µmdiameter circular mesa.

Fig. 11 .

 11 Fig. 11. Infrared image left and atomic force microscope surface profile of an oxidized AlAsSb13.4% 60-µm-diameter with the oxide aperture contours highlighted in blue. The oxidation conditions are 420°C & 500 mbar for 20 min.

Acknowledgments

This study was supported by, the ANR project "MIMICSEL" (ANR-16-CE24-0011), the ANR project "EAM-VCSELs" (ANR-19-CE24-0018) and Renatech, the French network of cleanroom facilities.

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Authors declarations

The authors declare no conflicts of interest.


Michel Généreux
email: genereux@lipn.univ-paris13.fr

Thierry Poibeau
email: poibeau@lipn.univ-paris13.fr

Moshe Koppel
email: koppel@cs.biu.ac.il

Sentiment analysis using automatically labelled financial news

Given a corpus of financial news labelled according to the market reaction following their publication, we investigate cotemporeneous and forward-looking price stock movements. Our approach is to provide a pool of relevant textual features to a machine learning algorithm to detect substantial stock price variations. Our two working hypotheses are that the market reaction to a news is a good indicator for labelling financial news, and that a machine learning algorithm can be trained on those news to build models detecting price movement effectively.

Introduction

The aim of this research is to build on work by [START_REF] Koppel | Good news or bad news? let the market decide[END_REF]) and (V. [START_REF] Lawrie | Mining of concurrent text and time series[END_REF] to investigate the subjective use of language in financial news about companies traded publicly and validate an automated labelling method. More precisely, we are interested in the short-term impact of financial news on the stock price of companies. This is a challenging task because although investors, to a certain extent, make their decision on the basis of factual information such as income statement, cash-flow statements or balance sheet analysis, there is an important part of their decision which is based on a subjective evaluation of events surrounding the activities of a company. Traditional Natural Language Processing (NLP) has so far been concerned with the objective use of language. However, the subjective aspect of human language, i.e. sentiment that cannot be directly inferred from a document's propositional content, has recently emerged as the new useful and insightful area of research in NLP [START_REF] Devitt | Sentiment polarity identification in financial news: A cohesion-based approach[END_REF][START_REF] Mishne | Applied text analytics for blogs[END_REF]. According to [START_REF] Wilson | Annotating opinions in the world press[END_REF], affective states include opinions, beliefs, thoughts, feelings, goal, sentiments, speculations, praise, criticism and judgements, to which we may add attitude (emotion, warning, stance, uncertainty, condition, cognition, intention and evaluation); they are at the core of subjectivity in human language. We treat short financial news about companies as if they were carrying implicit sentiment about future market direction made explicit by the vocabulary employed and investigate how this sentimental vocabulary can be automatically extracted from texts and used for classification. There are several reasons why we would want to do this, the most important being the potential of financial gain based on the exploitation of covert sentiment in the news for short-term investment. On a less pragmatic level, going beyond literal meaning in NLP would be of great theoretical interest for language practitioners in general, but most importantly perhaps, it would be of even greater interest for anyone who wishes to get a sense of what are people feelings towards a particular news, topic or concept. To achieve this we must overcome problems of ambiguity and context-dependency. Sentiment classification is often ambiguous (compare I had an accident, neg-ative with I met him by accident, not negative) and context dependent (There was a decline, negative for finance but positive for crimes).

Experiments

Based on previous work in sentiment analysis for domains such as movie reviews and blog posts, this first series of experiments aim at selecting an appropriate set of three key parameters in text classification: feature type, threshold and count. Our goal is to see whether the most suitable combinations usually employed for other domains can be successfully transferred to the financial domain. Our corpus is a subset of the one used in [START_REF] Koppel | Good news or bad news? let the market decide[END_REF] The automated labelling process is described in section 2.4. We have opted for a linear Support Vector Machine (SVM) [START_REF] Joachims | Learning to Classify Text Using Support Vector Machines[END_REF] approach as our classification algorithm with the software Weka1 . All experiments have been crossvalidated ten times.

Feature Types

We consider five types of features: unigrams, stems, financial terms, health-metaphors and agent-metaphors. The news are tokenized with the help of a POS tagger [START_REF] Schmid | Probabilistic part-of-speech tagging using decision trees[END_REF]. Unigrams consist of all nouns, verbs, adjectives and adverbs2 that appears at least three times in the corpus. Stems are the unigrams which have been stripped of their morphological variants. The financial terms stem from a clinical study of investors discussion and sentiment [START_REF] Das | e-information: A clinical study of investor discussion and sentiment[END_REF]. The list comprises 420 words and their variants created by graduate students who read through messages3 and selected words they felt were relevant for finance (not necessarily most frequent)4 . Health metaphors are a list of words identified by [START_REF] Knowles | Lexicigraphical aspects of health metaphors in financial texts[END_REF] in a six million word corpus from the Financial Times suggesting that the financial domain is pervaded by terms from the medical domain to describe market phenomena: examples include addiction, chronic and recovery. The full list comprises 123 such terms. Finally, recent work by [START_REF] Morris | Metaphors and the market: Consequences and preconditions of agent and object metaphors in stock market commentary[END_REF] shows that in the case of market trends, investors tend to process agent metaphors, when the language treats the market as though it were an entity that produces an effect deliberately (e.g. the NASDAQ climbed higher), differently from object metaphors, where the language describe price movements as object trajectories, as events in which inanimate objects are buffeted by external physical forces (e.g. the Dow fell through a resistance level) or non-metaphorical expressions that describe price change as increase/decrease or as closing up/down (e.g. the Dow today ended down almost 165 points). The same study gives the verbs jump, climb, recover and rally as the most frequent indicators of uptrend movement, and fall, tumbled, slip and struggle as the most frequent indicators of downtrend movements. The point made in the study is that in the case of agent metaphors, investors tend to believe that the market will continue moving in the same direction, which is not the case for object metaphors or non metaphors. These results are potentially useful for sentiment analysis, as we are trying to find positively correlated textual features with market trends. To construct a list of potential agents, we extracted all nouns from our corpus and used WordNet5 to filter out elements which were not hyponym of synset number 1000055986 , defined as an entity that produces an effect or is responsible for events or results: in this way we collected 553 potential agents. To allow those agents to carry out their actions, we completed this list with all 1538 verbs from the corpus.

Feature Selection

We consider three feature selection methods that [START_REF] Yang | A comparative study on feature selection in text categorization[END_REF] reported as providing excellent performance. Document Frequency (DF) is the number of documents in which a term occurs. We computed DF for each feature and eliminated features for which DF fell below a threshold (100). In Information Gain (IG), features are ranked according to a preferred sequence allowing the classifier to rapidly narrow down the set of classes to one single class. We computed the 100 features with the highest information gain. Finally, the χ 2 statistic measures the lack of independence between a feature and a set of classes. We computed the top 100 least independent features. It is worth mentioning that the same 100 features were selected using either IG or χ 2 statistic, except for a few features ranking order in the top ten.

Counting Methods

There are two methods worth considering for valuing each feature appearance in each news: the first is the binary method where a value of zero indicates the absence of the feature whereas a value of one indicates the presence of the feature. This method appears to yield good results in movie reviews [START_REF] Pang | Thumbs up? sentiment classification using machine learning techniques[END_REF]. The second simply gives a count of the feature in the document and normalise the count for a fixed-length document of 1000 words (TF).

Classifying news using cotemporeneous prices: [-1 day,+1 day]

To construct our 500 positive examples we used similar criteria as [START_REF] Koppel | Good news or bad news? let the market decide[END_REF], based on contemporaneous price changes (stock price at opening the first market trading day after the news was published -stock price at closing the first trading day before the news was published):

• price change superior to the overall S&P index price change,

• price change in the interval [-4%,+4%] and

• price superior to $10.

For instance, the following news about the company Biogen, Inc. (unigrams, IG and binary). That is, in each successive measurement of accuracy, at least two values of the trio remained unchanged. For example, the classification accuracy when using stems, information gain and binary count is 66.9%. Strictly speaking, the best combination (unigrams, IG and TF) reached 67.6%, a tenth of one percent better than the basic trio (unigrams, IG and Bin). Given this non significant difference in accuracy and a favourable inclination for the binary method in the literature, we keep the basic trio as our parameter values for all other experiments. These results also show that features based on a list of agent metaphors describing market trend movements appear more useful for the classification of financial news than a list of health metaphors or a humanconstructed list of financial terms. At closer examination, it appears that most of the contribution is made by the notion of agent: only five of the eight most frequent indicators (recover, climb, fall, slip and struggle) actually appear in our corpus, and only one (fall) made the cut through the top 100 features that bring most information gain. We conjecture that the description of financial news retains the same agent-based feature as in market trend description, however it is expressed by commentators using a different set of (predicative) terms. In the remaining experiments we depart slightly from [START_REF] Koppel | Good news or bad news? let the market decide[END_REF]) by taking into account negation, i.e. negated words (e.g. not rich) are featured as a single term (not rich). We also remove all proper nouns as potential feature, our assumption being that a list of features without proper nouns is less tailored to a particular time-period, where some companies happen to be more in the spotlight than others. Including Osgood's feature A study by [START_REF] Mullen | Sentiment analysis using support vector machines with diverse information sources[END_REF] have suggested that information from different sources can be used advantageously to support more traditional features. Typically, these features characterise the semantic orientation (SO) of a document as a whole [START_REF] Osgood | The Measurement of Meaning[END_REF][START_REF] Kamps | Using wordnet to measure semantic orientation of adjectives[END_REF]. One such feature is the result of summing up the semantic relatedness (Rel) between all individual words (adjectives, verbs, nouns and adverbs) with a set of polarised positive (P) and negative (N) terms, for the domain of interest, here finance. This method can be expressed in the following formal manner: Note that the quantity of positive terms P must be equal to the quantity of negative terms N. To compute relatedness, we used the method described in [START_REF] Banerjee | Extended gloss overlaps as a measure of semantic relatedness[END_REF] Using the PERL package [START_REF] Pedersen | Wordnet::similarity -measuring the relatedness of concepts[END_REF].

(
Although the relatedness measure is biased towards negative, as illustrated by all negative semantic orientations, even for positive classes, the trend observed and expected is that positive classes are less negative than positive classes in general (coefficient of correlation is +0.76). This is a result conforting the validity of the automatic labelling technique. However, our result shows no significant improvement on accuracy (69%) if we include semantic orientation as one of our features.

Horizon Effect

The next experiment looks at the lasting effect of a news on the stock price of a company. Using 300 positive examples and 300 negative examples with a ±2% price variation, we computed classification accuracies for non cotemporeneous, more precisely subsequent, price changes. Therefore, news were classified according to price changes from the opening the first open market day after the news to X number of days after the news. We consider the following values for X: 2, 3, 7, 14 and 28. we move further away from the day the news first broke out (coefficient of correlation is -0.89), we conclude that some prices are getting back to, or even at the opposite of, their initial level (i.e. before the news broke out). Assuming that in the interval no other news interfered with the stock price, this result also reinforced the validity of the automatic labelling technique.

Polarity effect

This experiment looks at the effect on accuracy a change in the labelling distance between two classes produces. The intuition is that the more distant two classes are from each other, the easiest it is for the classifier to distinguish among them, which translates as a higher accuracy. The five classes above generate four possible combinations of labelling distance: 0.25, 0.50, 0.75 and 1.00. As expected, there is a positive correlation between labelling distance and accuracy (coefficient of correlation +0.89). This reinforces the validity of the automatic labelling technique.

Range Effect

The range effect experiment explores how the size of the minimum price change for a news to be labelled either as positive or negative influences classification accuracy. The intuition is that the more positive and negative news are labelled according to a larger price change, the more accurate classification should be. , the more comfortable the price change margin gets, the more accurate classification is (coefficient of correlation is +0.86). However, accuracies appear to reach a plateau at around 6%, where classification accuracy improvements beyond 75% seems out of reach. The last column of table 5 reports accuracies for the case where news whose price change is falling between the range are labelled as neutral.

Although accuracies are, as expected, lower than for two classes, they are significantly above chance (33%). The same positive correlation is also observed between the price change margin and accuracies (coefficient of correlation is +0.88). In the next experiment we examine more in depth the effect of adding a neutral class on precision.

Effect of adding a neutral class on non-cotemporaneous prices: [+1 day,+2 days]

In all but one of the experiments so far, we have considered classes with maximum polarity, i.e. with a neutral class separating them. On one hand this has simplified the task of the classifier since news to be categorised belonged to one of the positive or negative extremes. On the other hand, this state of affairs is somewhat remote from situations occurring in real life, when the impact of news can be limited. Moreover, the information about overall accuracy of classification is not the most sought after information for investors. Let's examine briefly more useful information for investors:

Positive Precision A news which is correctly recognised as positive is a very important source of information for the investor. The potential winning strategy now available is to buy or hold the stock for the corresponding range. Therefore, it is very important to built a classifier with high precision for the positive class, significantly above 50% to cover comfortably transaction costs.

Negative Precision A news which is correctly recognised as negative is also an important source of information for the investor. The potential saving strategy now available to the investor, given that he or she owns the stock, is to sell the stock before it depreciates. Therefore, it is important to built a classifier with high precision for the negative class, significantly above 50% to cover safely transaction costs.

Positive and Negative Recall Ideally, all positive and negative news should be recognised, but given the potential substantial losses that misrecognition (implying low positive/negative precision) would imply for investors, only a decent level of recall is needed for both. precision is either worryingly close to 50% (the positive case), or is very volatile and could swing precision level well below 50% on too many occasions. Clearly, this demonstrate that if we are to build a financial news classifier satisfying at least high precision for the positive news, we must abandon the approach using three classes.

Conflating two classes

In section 2.8. we underlined the importance of high precision for the classification of positive and negative news and concluded that a 3-class categoriser was unlikely to satisfy this requirement. In this section we conflate two of the three classes into one and examine the effect on precision and recall. Table 7 displays three classification measures for the case where the classes neutral and negative have been conflated to a single class. Closer examination of the features resulting from the selection process paints a different picture from the one presented in [START_REF] Koppel | Good news or bad news? let the market decide[END_REF]. Recall that (Koppel and Shtrimberg, 2004) used all words that appeared at least sixty times in the corpus, eliminating function words with the exception of some relevant words. We kept only adjectives, common nouns, verbs, adverbs and four relevant words, above, below, up and down, that appear at least three times in the training corpus. In a nutshell, [START_REF] Koppel | Good news or bad news? let the market decide[END_REF] found that there were no markers for positive stories, which were characterised by the absence of negative markers. As a result, recall for positive stories were high but precision much lower. Our findings are that negative and positive features are approximately equally distributed (53 negatives and 47 positives) among the top 100 features with the highest information gain and that recall and precision for positive stories were respectively lower and higher. We define sentimental orientation (positive or negative) of each feature as the class in which the feature appears the most often.

Discussion

The surprisingly encouraging results we have presented for a forward-looking investment strategy should not be viewed outside its specific experimental setup conditions. In what follows we highlight a number of points worth considering:

Lack of independent testing corpus Cross-validation is a method which can provide a solid evaluation of the overall accuracy of a classifying method. However, a more accurate evaluation should involve an independent testing corpus, ideally covering a distant timeperiod to avoid overfitting or overtraining. Nevertheless, we have attempted to avoid these caveats by keeping a small number of features compared to the number of training examples and by avoiding the use of proper nouns as features.

Pool of features Our pool of features was selected among the entire training set, which includes the crossvalidated sections. Although to a small degree, this may have caused a data-snooping bias, where features were selected among the testing examples. On the other hand, as can be observed in tables 9 and 10, the interpretation of positive and negative features is not straightforward, which suggests that portability among different domains and even time periods could be problematic.

Size of documents Clearly, the size of documents is crucial for classification. The corpus we used averaged just over 71 words, which in general should be long enough to collect enough statistics. Nevertheless, if we look at our top ten positive stories (those with the highest positive price change), we found that half of them contained no feature at all, whereas three out of our top ten negative examples were similarly deprived of features. Given that this situation is likely to worsen if we train and test on different domains and periods, this is a potential area where a default bias can be difficult to avoid (i.e. a document without features will systematically be classified in the same class). One solution would be to increase the number of features.

Trading costs If the minimum transaction level to overcome fixed and relative trading costs is high, this brings upon the investors a burden of risk which he or she may not be able or willing to bear. The classifier should be characterised clearly by its level of precision matched with an estimate of the trading costs that would guide the investor in its decision.

Conclusion and future work

We have revisited a method for classifying financial news using automatically labelled data. Our findings give a different picture of the set of features best suited for the task and a somewhat less pessimistic prognostic as to the validity of such an approach for forward-looking investment.

We indicate a number of elements where extensive research should be carried on to test the approach within a practical and realistic framework. To this end, our next step is to use our system coupled with a virtual trading site 8 to monitor financial news to invest in companies. This should give us a better idea of the effect of the transaction costs as well as the portability of the features and model developed during our experiments.

  : 6277 news averaging 71 words covering 464 stocks listed in the Standard & Poor 500 for the years 2000-2002.

Table 1 :

 1 Feature tuning reference trio of parameters appears in table 1 between horizontal lines

	Features	F-Selection	F-Count
	Unig.	67.5% IG 67.5% Bin 67.5%
	Stems	66.9% DF 59.4% TF	67.6%
	Fin.-T	59.2% χ 2 66.1%
	Hea.-M 52.4%
	Age.-M 66.4%

symbol BGEN), appeared on May 23rd 2002: Biogen, Inc. announced that the FDA's Dermatologic & Ophthalmic Drug Advisory Committee voted to recommend approval of AMEVIVE (alefacept) for the treatment of moderate-tosevere chronic plaque psoriasis. At opening on the 24-May-2002, price reached $48.43, whereas at closing on the 22-May-2002 it was $38.71. Therefore, there is a positive price change of $48.43 -$38.71 $38.71 = 0.19995 or almost 20%, the news is classified as being positive. The same reasoning is applied to find 500 negative examples, corresponding to a negative price change of at least 4%. The results of this experience is presented in table 1. The

  and WordNet 7 . The list of polarised terms we used follows:

	Pos adjectives: good, rich
	Neg adjectives: bad, poor
	Pos nouns: goodness, richness
	Neg nouns: badness, poverty
	Pos verbs: increase, enrich
	Neg verbs: decrease, impoverish
	Pos adverbs: well, more
	Neg adverbs: badly, less
	Class SO
	0.00	-106
	0.25	-89
	0.50	-104
	0.75	-114
	1.00	-128
	Table 2: Semantic Orientation
	7

Table 3 :

 3 Table 3 presents the results. Given that classification accuracies are slowly worsening as Horizon Effect

	Horizon Accuracy
	[+1,+2]	69.5%
	[+1,+3]	68.8%
	[+1,+7]	67.5%
	[+1,+14]	68.0%
	[+1,+28]	66.3%

Table 5

 5

			shows results using cotem-
	poreneous price changes. The labelling method yields once
	Range Nb examples 2-class 3-class
	±0.02	1000	67.8% 46.3%
	±0.03	1000	67.1% 47.9%
	±0.05	800	69.5% 46.8%
	±0.06	600	74.0% 50.1%
	±0.07	400	76.3% 50.1%
	±0.10	200	75.0% 51.3%
		Table 5: Range Effect
	again expected results: for two classes (positive and neg-
	ative)

Table 6

 6

	Range Nb examples -Precision +Precision
	±0.01	1000	77%	51%
	±0.02	800	41%	53%
	±0.03	400	69%	54%

gives a first glimpse of the sort of positive (+precision) and negative (-precision) precision we can expect if we built a 3-class classifier. In order to get closer to real classification conditions, we remove the constraint that stock prices must be greater than $10. Results show that

Table 6 :

 6 Effect of adding a neutral class on noncotemporaneous prices

Table 8

 8

	Measure/Class POS NEG+NEU
	Precision	0.857	0.671
	Recall	0.555	0.908
	Accuracy		0.7313

displays three classification measures for the case where the classes neutral and positive have been conflated to a single class. We used a range of ±0.02, a forward-looking horizon of [+1,+2] days with 800 training examples. It is difficult to evaluate precisely what the cost of trading represents, but there seems to be enough margin of manoeuvrer to overcome this impediment, especially in the case of the positive classifier (table

7

).

Table 7 :

 7 Positive against all others

	Measure/Class NEG POS+NEU
	Precision	0.652	0.805
	Recall	0.870	0.535
	Accuracy	0.7025
	Table 8: Negative against all others
	2.10. Positive and Negative features

Table 9 :

 9 Table9shows the top ten positive features and table10shows the top ten negative features. The Pos column indicates the position of the Positive Features feature in the top 100 ranking resulting from the information gain screening. The +b/-b column displays the number of documents (examples) in which the feature appears at least once (+b for positive and -b for negative). The +tf/tf column displays the number of times the feature appears in the entire set of documents (+tf for positive and -tf for negative), while the +n/-n column displays the same values normalised to a constant document length of 1000 words.

	Pos	Feature	+b/-b +tf/-tf	+n/-n
	1	common	29/8	33/13 1318/390
	2	shares	33/11 48/17 2014/640
	3	cited	20/4	20/4	427/49
	5	reason	18/4	18/4	411/69
	8	direct	7/0	7/0	163/0
	9	repurchase	15/3	26/3	818/115
	10	authorised	17/4	18/5	596/177
	11	drug	6/0	6/0	114/0
	13	partially	6/0	6/0	89/0
	14	uncertainty	6/0	6/0	102/0

http://www.cs.waikato.ac.nz/ml/weka/

This list is augmented by the words up, down, above and below to follow[START_REF] Koppel | Good news or bad news? let the market decide[END_REF].

 3 The corpus was a random selection of texts from on Yahoo, Motley fool and other financial

sites. 4 Sanjiv Das, personal communication.

http://wordnet.princeton.edu/

causal agency#n#1, cause#n#4 and causal agent#n#1