PYTHIA: Employing lexical and semantic features for sentiment analysis

Ioannis Manoussos Katakis; Iraklis Varlamis; George Tsatsaronis

doi:10.1007/978-3-662-44845-8_32

PYTHIA: Employing lexical and semantic features for sentiment analysis

Ioannis Manoussos Katakis, Iraklis Varlamis, George Tsatsaronis

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Scopus citations

Abstract

Sentiment analysis methods aim at identifying the polarity of a piece of text, e.g., passage, review, snippet, by analyzing lexical features at the level of the terms or the sentences. However, many of the previous works do not utilize features that can offer a deeper understanding of the text, e.g., negation phrases. In this work we demonstrate a novel piece of software, namely PYTHIA¹, which combines semantic and lexical features at the term and sentence level and integrates them into machine learning models in order to predict the polarity of the input text. Experimental evaluation of PYTHIA in a benchmark movie reviews dataset shows that the suggested combination performs favorably against previous related methods. An online demo is publicly available at http://omiotis.hua.gr/pythia.

Original language	English
Title of host publication	Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2014, Proceedings
Publisher	Springer Verlag
Pages	448-451
Number of pages	4
Edition	PART 3
ISBN (Print)	9783662448441
DOIs	https://doi.org/10.1007/978-3-662-44845-8_32
State	Published - 2014
Externally published	Yes
Event	European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2014 - Nancy, France Duration: Sep 15 2014 → Sep 19 2014

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 3
Volume	8726 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2014
Country/Territory	France
City	Nancy
Period	09/15/14 → 09/19/14

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10.1007/978-3-662-44845-8_32

Cite this

Katakis, I. M., Varlamis, I., & Tsatsaronis, G. (2014). PYTHIA: Employing lexical and semantic features for sentiment analysis. In Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2014, Proceedings (PART 3 ed., pp. 448-451). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8726 LNAI, No. PART 3). Springer Verlag. https://doi.org/10.1007/978-3-662-44845-8_32

Katakis, Ioannis Manoussos ; Varlamis, Iraklis ; Tsatsaronis, George. / PYTHIA : Employing lexical and semantic features for sentiment analysis. Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2014, Proceedings. PART 3. ed. Springer Verlag, 2014. pp. 448-451 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 3).

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abstract = "Sentiment analysis methods aim at identifying the polarity of a piece of text, e.g., passage, review, snippet, by analyzing lexical features at the level of the terms or the sentences. However, many of the previous works do not utilize features that can offer a deeper understanding of the text, e.g., negation phrases. In this work we demonstrate a novel piece of software, namely PYTHIA1, which combines semantic and lexical features at the term and sentence level and integrates them into machine learning models in order to predict the polarity of the input text. Experimental evaluation of PYTHIA in a benchmark movie reviews dataset shows that the suggested combination performs favorably against previous related methods. An online demo is publicly available at http://omiotis.hua.gr/pythia.",

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Katakis, IM, Varlamis, I & Tsatsaronis, G 2014, PYTHIA: Employing lexical and semantic features for sentiment analysis. in Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2014, Proceedings. PART 3 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 3, vol. 8726 LNAI, Springer Verlag, pp. 448-451, European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2014, Nancy, France, 09/15/14. https://doi.org/10.1007/978-3-662-44845-8_32

PYTHIA: Employing lexical and semantic features for sentiment analysis. / Katakis, Ioannis Manoussos; Varlamis, Iraklis; Tsatsaronis, George.
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2014, Proceedings. PART 3. ed. Springer Verlag, 2014. p. 448-451 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8726 LNAI, No. PART 3).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - Sentiment analysis methods aim at identifying the polarity of a piece of text, e.g., passage, review, snippet, by analyzing lexical features at the level of the terms or the sentences. However, many of the previous works do not utilize features that can offer a deeper understanding of the text, e.g., negation phrases. In this work we demonstrate a novel piece of software, namely PYTHIA1, which combines semantic and lexical features at the term and sentence level and integrates them into machine learning models in order to predict the polarity of the input text. Experimental evaluation of PYTHIA in a benchmark movie reviews dataset shows that the suggested combination performs favorably against previous related methods. An online demo is publicly available at http://omiotis.hua.gr/pythia.

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Katakis IM, Varlamis I, Tsatsaronis G. PYTHIA: Employing lexical and semantic features for sentiment analysis. In Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2014, Proceedings. PART 3 ed. Springer Verlag. 2014. p. 448-451. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 3). doi: 10.1007/978-3-662-44845-8_32

PYTHIA: Employing lexical and semantic features for sentiment analysis

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