Semantic patterns for sentiment analysis of Twitter

Hassan Saif*, Yulan He, Miriam Fernández, Harith Alani

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Most existing approaches to Twitter sentiment analysis assume that sentiment is explicitly expressed through affective words. Nevertheless, sentiment is often implicitly expressed via latent semantic relations, patterns and dependencies among words in tweets. In this paper, we propose a novel approach that automatically captures patterns of words of similar contextual semantics and sentiment in tweets. Unlike previous work on sentiment pattern extraction, our proposed approach does not rely on external and fixed sets of syntactical templates/patterns, nor requires deep analyses of the syntactic structure of sentences in tweets. We evaluate our approach with tweet- and entity-level sentiment analysis tasks by using the extracted semantic patterns as classification features in both tasks. We use 9 Twitter datasets in our evaluation and compare the performance of our patterns against 6 state-of-the-art baselines. Results show that our patterns consistently outperform all other baselines on all datasets by 2.19% at the tweet-level and 7.5% at the entity-level in average F-measure.

Original languageEnglish
Title of host publicationThe Semantic Web – ISWC 2014
Subtitle of host publication13th International Semantic Web Conference, Riva del Garda, Italy, October 19-23, 2014, proceedings, part II
EditorsPeter Mika, Tania Tudorache, Abraham Bernstein, Chris Welty, et al
Place of PublicationChem (CH)
PublisherSpringer
Pages324-340
Number of pages17
ISBN (Electronic)978-3-319-11915-1
ISBN (Print)978-3-319-11914-4
DOIs
Publication statusPublished - 31 Dec 2014
Event13th International Semantic Web Conference - Riva del Garda, Italy
Duration: 19 Oct 201423 Oct 2014

Publication series

NameLecture notes in computer science
PublisherSpringer
Volume8797
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference13th International Semantic Web Conference
Abbreviated titleISWC 2014
CountryItaly
CityRiva del Garda
Period19/10/1423/10/14

Fingerprint

Sentiment Analysis
Semantics
Syntactics
Baseline
Template
Evaluate
Evaluation

Keywords

  • semantic patterns
  • sentiment analysis
  • Twitter

Cite this

Saif, H., He, Y., Fernández, M., & Alani, H. (2014). Semantic patterns for sentiment analysis of Twitter. In P. Mika, T. Tudorache, A. Bernstein, C. Welty, & et al (Eds.), The Semantic Web – ISWC 2014: 13th International Semantic Web Conference, Riva del Garda, Italy, October 19-23, 2014, proceedings, part II (pp. 324-340). (Lecture notes in computer science; Vol. 8797). Chem (CH): Springer. https://doi.org/10.1007/978-3-319-11915-1_21
Saif, Hassan ; He, Yulan ; Fernández, Miriam ; Alani, Harith. / Semantic patterns for sentiment analysis of Twitter. The Semantic Web – ISWC 2014: 13th International Semantic Web Conference, Riva del Garda, Italy, October 19-23, 2014, proceedings, part II. editor / Peter Mika ; Tania Tudorache ; Abraham Bernstein ; Chris Welty ; et al. Chem (CH) : Springer, 2014. pp. 324-340 (Lecture notes in computer science).
@inproceedings{589b588b95b84a7dbefa8e659342def3,
title = "Semantic patterns for sentiment analysis of Twitter",
abstract = "Most existing approaches to Twitter sentiment analysis assume that sentiment is explicitly expressed through affective words. Nevertheless, sentiment is often implicitly expressed via latent semantic relations, patterns and dependencies among words in tweets. In this paper, we propose a novel approach that automatically captures patterns of words of similar contextual semantics and sentiment in tweets. Unlike previous work on sentiment pattern extraction, our proposed approach does not rely on external and fixed sets of syntactical templates/patterns, nor requires deep analyses of the syntactic structure of sentences in tweets. We evaluate our approach with tweet- and entity-level sentiment analysis tasks by using the extracted semantic patterns as classification features in both tasks. We use 9 Twitter datasets in our evaluation and compare the performance of our patterns against 6 state-of-the-art baselines. Results show that our patterns consistently outperform all other baselines on all datasets by 2.19{\%} at the tweet-level and 7.5{\%} at the entity-level in average F-measure.",
keywords = "semantic patterns, sentiment analysis, Twitter",
author = "Hassan Saif and Yulan He and Miriam Fern{\'a}ndez and Harith Alani",
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Saif, H, He, Y, Fernández, M & Alani, H 2014, Semantic patterns for sentiment analysis of Twitter. in P Mika, T Tudorache, A Bernstein, C Welty & et al (eds), The Semantic Web – ISWC 2014: 13th International Semantic Web Conference, Riva del Garda, Italy, October 19-23, 2014, proceedings, part II. Lecture notes in computer science, vol. 8797, Springer, Chem (CH), pp. 324-340, 13th International Semantic Web Conference , Riva del Garda, Italy, 19/10/14. https://doi.org/10.1007/978-3-319-11915-1_21

Semantic patterns for sentiment analysis of Twitter. / Saif, Hassan; He, Yulan; Fernández, Miriam; Alani, Harith.

The Semantic Web – ISWC 2014: 13th International Semantic Web Conference, Riva del Garda, Italy, October 19-23, 2014, proceedings, part II. ed. / Peter Mika; Tania Tudorache; Abraham Bernstein; Chris Welty; et al. Chem (CH) : Springer, 2014. p. 324-340 (Lecture notes in computer science; Vol. 8797).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AU - He, Yulan

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AU - Alani, Harith

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N2 - Most existing approaches to Twitter sentiment analysis assume that sentiment is explicitly expressed through affective words. Nevertheless, sentiment is often implicitly expressed via latent semantic relations, patterns and dependencies among words in tweets. In this paper, we propose a novel approach that automatically captures patterns of words of similar contextual semantics and sentiment in tweets. Unlike previous work on sentiment pattern extraction, our proposed approach does not rely on external and fixed sets of syntactical templates/patterns, nor requires deep analyses of the syntactic structure of sentences in tweets. We evaluate our approach with tweet- and entity-level sentiment analysis tasks by using the extracted semantic patterns as classification features in both tasks. We use 9 Twitter datasets in our evaluation and compare the performance of our patterns against 6 state-of-the-art baselines. Results show that our patterns consistently outperform all other baselines on all datasets by 2.19% at the tweet-level and 7.5% at the entity-level in average F-measure.

AB - Most existing approaches to Twitter sentiment analysis assume that sentiment is explicitly expressed through affective words. Nevertheless, sentiment is often implicitly expressed via latent semantic relations, patterns and dependencies among words in tweets. In this paper, we propose a novel approach that automatically captures patterns of words of similar contextual semantics and sentiment in tweets. Unlike previous work on sentiment pattern extraction, our proposed approach does not rely on external and fixed sets of syntactical templates/patterns, nor requires deep analyses of the syntactic structure of sentences in tweets. We evaluate our approach with tweet- and entity-level sentiment analysis tasks by using the extracted semantic patterns as classification features in both tasks. We use 9 Twitter datasets in our evaluation and compare the performance of our patterns against 6 state-of-the-art baselines. Results show that our patterns consistently outperform all other baselines on all datasets by 2.19% at the tweet-level and 7.5% at the entity-level in average F-measure.

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Saif H, He Y, Fernández M, Alani H. Semantic patterns for sentiment analysis of Twitter. In Mika P, Tudorache T, Bernstein A, Welty C, et al, editors, The Semantic Web – ISWC 2014: 13th International Semantic Web Conference, Riva del Garda, Italy, October 19-23, 2014, proceedings, part II. Chem (CH): Springer. 2014. p. 324-340. (Lecture notes in computer science). https://doi.org/10.1007/978-3-319-11915-1_21