A machine-learning approach to negation and speculation detection for sentiment analysis

Recognizing negative and speculative information is highly relevant for sentiment analysis. This paper presents a machine-learning approach to automatically detect this kind of information in the review domain. The resulting system works in two steps: in the first pass, negation/speculation cues are identified, and in the second phase the full scope of these cues is determined. The system is trained and evaluated on the Simon Fraser University Review corpus, which is extensively used in opinion mining. The results show how the proposed method outstrips the baseline by as much as roughly 20% in the negation cue detection and around 13% in the scope recognition, both in terms of F1. In speculation, the performance obtained in the cue prediction phase is close to that obtained by a human rater carrying out the same task. In the scope detection, the results are also promising and represent a substantial improvement on the baseline (up by roughly 10%). A detailed error analysis is also provided. The extrinsic evaluation shows that the correct identification of cues and scopes is vital for the task of sentiment analysis.Maite Taboada from the Natural Sciences and Engineering Research Council of Canada (Discovery Grant 261104- 2008). This work was partly funded by the Spanish Ministry of Education and Science (TIN2009-14057-C03-03 Project) and the Andalusian Ministry of Economy, Innovation and Science (TIC 07629 and TIC 07684 Projects)

An Empirical Analysis of the Role of Amplifiers, Downtoners, and Negations in Emotion Classification in Microblogs

The effect of amplifiers, downtoners, and negations has been studied in general and particularly in the context of sentiment analysis. However, there is only limited work which aims at transferring the results and methods to discrete classes of emotions, e. g., joy, anger, fear, sadness, surprise, and disgust. For instance, it is not straight-forward to interpret which emotion the phrase "not happy" expresses. With this paper, we aim at obtaining a better understanding of such modifiers in the context of emotion-bearing words and their impact on document-level emotion classification, namely, microposts on Twitter. We select an appropriate scope detection method for modifiers of emotion words, incorporate it in a document-level emotion classification model as additional bag of words and show that this approach improves the performance of emotion classification. In addition, we build a term weighting approach based on the different modifiers into a lexical model for the analysis of the semantics of modifiers and their impact on emotion meaning. We show that amplifiers separate emotions expressed with an emotion- bearing word more clearly from other secondary connotations. Downtoners have the opposite effect. In addition, we discuss the meaning of negations of emotion-bearing words. For instance we show empirically that "not happy" is closer to sadness than to anger and that fear-expressing words in the scope of downtoners often express surprise.Comment: Accepted for publication at The 5th IEEE International Conference on Data Science and Advanced Analytics (DSAA), https://dsaa2018.isi.it

Scope of negation detection in sentiment analysis

An important part of information-gathering behaviour has always been to find out what other people think and whether they have favourable (positive) or unfavourable (negative) opinions about the subject. This survey studies the role of negation in an opinion-oriented information-seeking system. We investigate the problem of determining the polarity of sentiments in movie reviews when negation words, such as not and hardly occur in the sentences. We examine how different negation scopes (window sizes) affect the classification accuracy. We used term frequencies to evaluate the discrimination capacity of our system with different window sizes. The results show that there is no significant difference in classification accuracy when different window sizes have been applied. However, negation detection helped to identify more opinion or sentiment carrying expressions. We conclude that traditional negation detection methods are inadequate for the task of sentiment analysis in this domain and that progress is to be made by exploiting information about how opinions are expressed implicitly

Detección de la Negación y la Especulación en Textos Médicos y de Opinión

PhD Thesis written by Noa P. Cruz Díaz at the University of Huelva under the supervision of Dr. Manuel J. Maña López. The author was examined on 10th July 2014 by a committee formed by the doctors Manuel de Buenaga (European University of Madrid), Mariana Lara Neves (University of Berlin) and Jacinto Mata (University of Huelva). The PhD Thesis was awarded Summa cum laude (International Doctorate).Tesis doctoral realizada por Noa P. Cruz Díaz en la Universidad de Huelva bajo la dirección del Dr. Manuel J. Maña López. El acto de defensa tuvo lugar el jueves 10 de julio de 2014 ante el tribunal formado por los doctores Manuel de Buenaga (Universidad Europea de Madrid), Mariana Lara Neves (Universidad de Berlín) y Jacinto Mata (Universidad de Huelva). Obtuvo mención internacional y la calificación de Sobresaliente Cum Laude por unanimidad.This thesis has been funded by the University of Huelva (PP10-02 PhD Scholarship), the Spanish Ministry of Education and Science (TIN2009-14057-C03-03 Project) and the Andalusian Ministry of Economy, Innovation and Science (TIC 07629 Project)

Identifying Emotions in Social Media: Comparison of Word-emotion lexica

In recent years, emotions expressed in social media messages have become a vivid research topic due to their influence on the spread of misinformation and online radicalization over online social networks. Thus, it is important to correctly identify emotions in order to make inferences from social media messages. In this paper, we report on the performance of three publicly available word-emotion lexicons (NRC, DepecheMood, EmoSenticNet) over a set of Facebook and Twitter messages. To this end, we designed and implemented an algorithm that applies natural language processing (NLP) techniques along with a number of heuristics that reflect the way humans naturally assess emotions in written texts. In order to evaluate the appropriateness of the obtained emotion scores, we conducted a questionnaire-based survey with human raters. Our results show that there are noticeable differences between the performance of the lexicons as well as with respect to emotion scores the human raters provided in our surve