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

Cruz, Noa P.; Taboada, Maïté; Mitkov, Ruslan

doi:10.1002/asi.23533

Cited by 56 publications

(34 citation statements)

References 40 publications

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“…(past tense of can) K 3. caused additional difficulties for the classifiers, and might be the reason why the baseline classifier, which used cues from the entire sentence, outperformed the chunk-based one for the category Hypotheticality. Many previous studies (for instance [4,8,14], but not [17]) have (i) grouped Hypotheticality and Uncertainty into one category, and (ii) treated, e.g., "think"/"should"/"could" as markers for Uncertainty regardless of the pragmatics (Ex. 1.3/1.4/2.4/3.4), which might explain why lower results were achieved here than in previous studies (Table 1).…”

Section: Resultsmentioning

confidence: 99%

Detection of Stance and Sentiment Modifiers in Political Blogs

Skeppstedt

Simaki

Paradis

et al. 2017

Speech and Computer

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Abstract. The automatic detection of seven types of modifiers was studied: Certainty, Uncertainty, Hypotheticality, Prediction, Recommendation, Concession/Contrast and Source. A classifier aimed at detecting local cue words that signal the categories was the most successful method for five of the categories. For Prediction and Hypotheticality, however, better results were obtained with a classifier trained on tokens and bigrams present in the entire sentence. Unsupervised cluster features were shown useful for the categories Source and Uncertainty, when a subset of the training data available was used. However, when all of the 2,095 sentences that had been actively selected and manually annotated were used as training data, the cluster features had a very limited effect. Some of the classification errors made by the models would be possible to avoid by extending the training data set, while other features and feature representations, as well as the incorporation of pragmatic knowledge, would be required for other error types.

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Section: Resultsmentioning

confidence: 99%

Detection of Stance and Sentiment Modifiers in Political Blogs

Skeppstedt

Simaki

Paradis

et al. 2017

Speech and Computer

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“…Modern and emerging strategies of sentiment detection make use of text illustration nested on semantic vector spaces. Many researchers have used machine learning approaches to detect negation and prediction in sentiment analysis [15]. According to recent reports published in 2019 [13], the quantity of non-English pages is swiftly growing because the growth rate of English Web sites is much lower than many other languages such as Arabic, Chinese, or Spanish.…”

Section: Evaluation Of Sentimental Analysis and Its Achievementsmentioning

confidence: 99%

Sentiment analysis on IMDB using lexicon and neural networks

et al. 2020

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To find out what other people think has been an essential part of information-gathering behaviors. And in the case of movies, the movie reviews can provide an intricate insight into the movie and can help decide whether it is worth spending time on. However, with the growing amount of data in reviews, it is quite prudent to automate the process, saving on time. Sentiment analysis is an important field of study in machine learning that focuses on extracting information of subject from the textual reviews. The area of analysis of sentiments is related closely to natural language processing and text mining. It can successfully be used to determine the attitude of the reviewer in regard to various topics or the overall polarity of the review. In the case of movie reviews, along with giving a rating in numeric to a movie, they can enlighten us on the favorableness or the opposite of a movie quantitatively; a collection of those then gives us a comprehensive qualitative insight on different facets of the movie. Opinion mining from movie reviews can be challenging due to the fact that human language is rather complex, leading to situations where a positive word has a negative connotation and vice versa. In this study, the task of opinion mining from movie reviews has been achieved with the use of neural networks trained on the "Movie Review Database" issued by Stanford, in conjunction with two big lists of positive and negative words. The trained network managed to achieve a final accuracy of 91%.

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“…Taboada et al discuss lexicon-based methods for sentiment analysis in a broader context [31]. More recent work developed machine-learning-based classifiers to detect speculation and negations particularly for sentiment analysis [32].…”

Section: B Modifier Detection For Sentiment and Opinion Analysismentioning

confidence: 99%

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

Strohm¹,

Klinger

2018

2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA)

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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 emotionbearing 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.

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

Cited by 56 publications

References 40 publications

Detection of Stance and Sentiment Modifiers in Political Blogs

Detection of Stance and Sentiment Modifiers in Political Blogs

Sentiment analysis on IMDB using lexicon and neural networks

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

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