A lexicon model for deep sentiment analysis and opinion mining applications

Maks, Isa; Vossen, Piek

doi:10.1016/j.dss.2012.05.025

Cited by 154 publications

(64 citation statements)

References 10 publications

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“…Many opinion verbs predominantly express the sentiment of the speaker of the utterance (or some nested source) (4). This concept is also known as expressive subjectivity or speaker subjectivity (Maks and Vossen, 2012). In such opinions, the opinion holder is not realized as a dependent of the opinion verb.…”

Section: Lexicon-based Opinion Role Extractionmentioning

confidence: 99%

Opinion Holder and Target Extraction based on the Induction of Verbal Categories

Wiegand¹,

Ruppenhofer²

2015

Proceedings of the Nineteenth Conference on Computational Natural Language Learning

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We present an approach for opinion role induction for verbal predicates. Our model rests on the assumption that opinion verbs can be divided into three different types where each type is associated with a characteristic mapping between semantic roles and opinion holders and targets. In several experiments, we demonstrate the relevance of those three categories for the task. We show that verbs can easily be categorized with semi-supervised graphbased clustering and some appropriate similarity metric. The seeds are obtained through linguistic diagnostics. We evaluate our approach against a new manuallycompiled opinion role lexicon and perform in-context classification.

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Section: Lexicon-based Opinion Role Extractionmentioning

confidence: 99%

Opinion Holder and Target Extraction based on the Induction of Verbal Categories

Wiegand¹,

Ruppenhofer²

2015

Proceedings of the Nineteenth Conference on Computational Natural Language Learning

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“…However, verbs and nouns are capable as well of conveying emotions and sentiments. The analysis of verbs to create a verb lexicon that would aid with establishing sentiments in opinion mining applications is explored in [43], while a combination of adjective, verbs and adverbs in an effort to improve subjectivity classification is presented in [42]. Using all of these three components of part-of-speech simultaneously may contribute to a more reliable subjectivity classification process.…”

Section: Through 2014mentioning

confidence: 99%

Main Concepts, State of the Art and Future Research Questions in Sentiment Analysis

Appel

Chiclana

Carter

2015

APH

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“…[7] combined ANEW [12] and WordNet [13] to create a superior lexicon. [8] developed a domain-specific lexicon using a random walk algorithm, while [14] extended a lexicon with additional knowledge, such as parts of speech and word senses. The continual emergence of similar methods for a known problem shows a satisfactory solution has still not been found.…”

Section: Related Workmentioning

confidence: 99%

Constructing Context-Aware Sentiment Lexicons with an Asynchronous Game with a Purpose

Boia

Musat

Faltings

2014

Computational Linguistics and Intelligent Text Processing

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Abstract. One of the reasons sentiment lexicons do not reach human-level performance is that they lack the contexts that define the polarities of words. While obtaining this knowledge through machine learning would require huge amounts of data, context is commonsense knowledge for people, so human computation is a better choice. We identify context using a game with a purpose that increases the workers' engagement in this complex task. With the contextual knowledge we obtain from only a small set of answers, we already halve the sentiment lexicons' performance gap relative to human performance.

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A lexicon model for deep sentiment analysis and opinion mining applications

Cited by 154 publications

References 10 publications

Opinion Holder and Target Extraction based on the Induction of Verbal Categories

Opinion Holder and Target Extraction based on the Induction of Verbal Categories

Main Concepts, State of the Art and Future Research Questions in Sentiment Analysis

Constructing Context-Aware Sentiment Lexicons with an Asynchronous Game with a Purpose

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