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2018 | OriginalPaper | Buchkapitel

Twitter Sentiment Analysis Experiments Using Word Embeddings on Datasets of Various Scales

verfasst von : Yusuf Arslan, Dilek Küçük, Aysenur Birturk

Erschienen in: Natural Language Processing and Information Systems

Verlag: Springer International Publishing

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Abstract

Sentiment analysis is a popular research topic in social media analysis and natural language processing. In this paper, we present the details and evaluation results of our Twitter sentiment analysis experiments which are based on word embeddings vectors such as word2vec and doc2vec, using an ANN classifier. In these experiments, we utilized two publicly available sentiment analysis datasets and four smaller datasets derived from these datasets, in addition to a publicly available trained vector model over 400 million tweets. The evaluation results are accompanied with discussions and future research directions based on the current study. One of the main conclusions drawn from the experiments is that filtering out the emoticons in the tweets could be a facilitating factor for sentiment analysis on tweets.

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Vorheriges Kapitel Identifying and Classifying Influencers in Twitter only with Textual Information

Nächstes Kapitel A Deep Learning Approach for Sentiment Analysis Applied to Hotel’s Reviews

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Titel: Twitter Sentiment Analysis Experiments Using Word Embeddings on Datasets of Various Scales
verfasst von: Yusuf Arslan
Dilek Küçük
Aysenur Birturk
Verlag: Springer International Publishing
Buch: Natural Language Processing and Information Systems
Print ISBN: 978-3-319-91946-1

Electronic ISBN: 978-3-319-91947-8

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-91947-8_4

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