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Sentiment Polarity Identification in Financial News:A Cohesion-based Approach

Author: Ann Devitt

Khurshid Ahmad

(School of Computer Science & Statistics,

Trinity College Dublin, Ireland)

Source: ACL 2007

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Motivation & Goal

• Both the informational and affective aspects of news text affect the markets in profound ways

• The goal is to explore a computable metric of positive or negative polarity in financial news text

1. which is consistent with human judgments 2. can be used in a quantitative analysis of

news sentiment impact on financial markets

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Why the issue is important?

• Prospect Theory• Herbert Simon (1978 Nobel Prize) and Daniel

Kahneman (2002 Prize) – shows that people in markets can behave irrationally and that th

is bounded rationality is inspired by hear from others about the conditions

• Robert Engle (2003 Prize) – positive news is typically related to large changes in prices but o

nly for a short time– conversely the effect of negative news on prices and volumes of

trading is longer lasting

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Idea

• The contribution to an overall sentiment polarity and intensity metric of individual lexical items is determined by their connectivity and position within a representation of the text based on the principles of lexical cohesion

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Related Work

• Cognitive Theories of Emotion – what constitutes emotion?

• Categorical – e.g. anger, fear, sadness, surprise…

• Or Dimensional – Evaluation: good–bad axis– Activation: strong-weak axis

• Sentiment Analysis – computational linguistics focused on examining

• what textual features (lexical, syntactic, punctuation, etc) contribute to affective content of text

• how these features derive a sentiment metric for a word, sentence or whole text.

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Related Work

• Sentiment and News Impact Analysis– Niederhoffer (1971) analysed 20 years of New York Ti

mes headlines• classified into 19 semantic categories and on a good-bad rati

ng scale • to evaluate how the markets reacted to good and bed news • found that markets overreact to bad news

– Davis et al (2006) investigate the effects of optimistic or pessimistic language used in financial press releases on future firm performance

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Cohesion-based Text Representation Algorithm

• builds a graph representation of text from part-of-speech tagged text

• without disambiguation

• using WordNet as a real world knowledge source

• to reduce information loss in the transition from text to text-based structure

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Cohesive structureof a text

• The representation is designed within the theoretical framework of lexical cohesion (Halliday and Hasan, 1976)

• Graph representation combines:1. information derived from the text2. and WordNet semantic content

– Nodes: concepts in or derived from the text connected by relations between these concepts in WordNet• such as antonymy or hypernymy

– Topological features of the knowledge base provides the facility to manipulate or control how the WordNet semantic content information is interpreted

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Graphs of subtopics

• Skorochod'ko (1972) suggested to view the cohesion in a document as a sequence of densely interrelated subtopics

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Sentiment Polarity Overlay

• SentiWordNet was used as it provides positive and negative polarity value for a set of “affective” terms which conflates Osgood’s (1957) evaluative and activation dimensions

• The contribution of individual polarity nodes to the polarity metric of the text as a whole is determined with 1. respect to the textual information2. and WN semantic 3. and topological features encoded in the underlying g

raph representation of the text

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Three polarity metrics

1. Basic Cohesion Metric2. Relation Type Metric 3. Node Specificity Metric

• Basic Cohesion Metric – based solely on frequency of sentiment-bear

ing nodes in or derived from the source text • i.e. the sum of polarity values for all nodes in the

graph

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Three polarity metrics

• Relation Type Metric – with respect to the types of WordNet relations

in the text-derived graph – sentiment value of each node is the product of

its polarity value and a relation weight for each relation this node enters into in the graph structure

– Potentially affect-effecting relations have the strongest weighting: antonymy, hypernymy

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Three polarity metrics

• Node Specificity Metric • with respect to a measure of node specificity calculated

on the basis of topographical features of WordNet – Highly specific nodes or concepts may carry more informational

• specificity measure: – factor out population sparseness or density in WordNet by eval

uating1. the contribution of each node relative to its depth in the hierarchy2. its connectivity (branching Factor)3. its siblings

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Further specialised accordingto the following two boolean flags

• InText:– consider potentially noisy contributions from n

odes not in the source text – the metric is calculated based on

• 1) only those nodes representing terms in the source text, or

• 2) all nodes in the graph representation derived from the text

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Further specialised accordingto the following two boolean flags

• Modifiers: – In SentiWN, it seems that modifiers have mor

e reliable values than nouns or verbs – the metric is calculated using

• 1) all open class POS or • 2) modifiers alone

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Evaluation • The goal is to examine the relationship between financial markets a

nd financial news– to establishing reliability for a sentiment measure of news – the quantitative measure of polarity must be consistent with human judg

ments of polarity • Not all news items may express overt sentiment

– selected a news topic which was considered a priori to have emotive content

• Corpus – takeovers and mergers are usually seen as highly emotive contexts – the topic of a corpus of news texts spans 4 months in 2006 : aggressive

takeover bid of a low-cost airline (Ryanair) for the Irish flag-carrier airline (Aer Lingus)

– Both airlines have a strong (positive and negative) emotional attachment – Collect news through the “surprise” take-over bid announcement by Rya

nair, to the withdrawal of the bid by Ryanair in December 2006

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Evaluation

• Gold Standard – A set of 30 texts selected from the corpus– annotated by 3 people – on a 7-point scale from very positive to very

negative – the respondents were asked also to rate the

semantic orientation of the texts with respect to the two takeover bid players

– the respondents were first asked to rate their personal attitude to the two airlines

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Evaluation

• Performance Metrics – The performance of the polarity algorithm w

as evaluated on: 1. Polarity direction: the task of assigning a binary p

ositive/negative value to a text – Performance is reported using standard recall and preci

sion metrics

2. Polarity intensity: the task of assigning a value to indicate the strength of the negative/positive polarity in a text – Performance is reported as a correlation with average h

uman ratings

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Evaluation

• Baseline – the baseline for comparison sums the SentiW

N polarity rating for only those lexical items present in the text, not exploiting any aspect of the graph representation of the text

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Results and Discussion

• Binary Polarity Assignment

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Polarity Intensity Values

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Conclusions and Future Work

• This research is expected to contribute to sentiment analysis in finance

• To capture a psychologically plausible measure of affective content of text by exploiting available resources

• To broader evaluation of the measure relative to human judgments and existing metrics