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TweetSenti: Target-dependent Tweet Sentiment Analysis

Published: 13 May 2019 Publication History

Abstract

TweetSenti is a system for analyzing the sentiment of an entity in tweets. A sentence or tweet may contain multiple entities, and they do not always have the same sentiment polarity. Therefore, it is necessary to detect the sentiment for a specific target entity. This type of target-dependent (entity level) sentiment analysis has become attractive and has been used in many applications, but it is still a challenging task. TweetSenti employs a new approach for detecting the entity level sentiment. Our model splits a sentence into a left context and a right context according to the target entity, and it also exploits two different types of word embeddings to represent a word, the general word embedding and the sentiment specific word embedding. A hybrid neural network is used to capture both the sequence and structure information of the two sides of the target entity. The sequence information is learned by attention-based bi-directional LSTM models. The structure information is captured by multi-context CNN models. Based on this algorithm, we built a web-based application that users can interact with and analyze an entity's sentiment in Twitter at real-time.

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Cited By

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  • (2022)Event Detection from Social Media Stream: Methods, Datasets and Opportunities2022 IEEE International Conference on Big Data (Big Data)10.1109/BigData55660.2022.10020411(3509-3516)Online publication date: 17-Dec-2022
  • (2022)Rating Patent by Exploiting Semantic and Novelty Information2022 IEEE International Conference on Big Data (Big Data)10.1109/BigData55660.2022.10020300(3517-3523)Online publication date: 17-Dec-2022
  • (2021)A novel approach to stance detection in social media tweets by fusing ranked lists and sentimentsInformation Fusion10.1016/j.inffus.2020.10.00367(29-40)Online publication date: Mar-2021

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cover image ACM Other conferences
WWW '19: The World Wide Web Conference
May 2019
3620 pages
ISBN:9781450366748
DOI:10.1145/3308558
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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  • IW3C2: International World Wide Web Conference Committee

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 May 2019

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Author Tags

  1. Target-dependent sentiment
  2. Twitter
  3. entity level sentiment classification
  4. social media

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  • Refereed limited

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WWW '19
WWW '19: The Web Conference
May 13 - 17, 2019
CA, San Francisco, USA

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

View all
  • (2022)Event Detection from Social Media Stream: Methods, Datasets and Opportunities2022 IEEE International Conference on Big Data (Big Data)10.1109/BigData55660.2022.10020411(3509-3516)Online publication date: 17-Dec-2022
  • (2022)Rating Patent by Exploiting Semantic and Novelty Information2022 IEEE International Conference on Big Data (Big Data)10.1109/BigData55660.2022.10020300(3517-3523)Online publication date: 17-Dec-2022
  • (2021)A novel approach to stance detection in social media tweets by fusing ranked lists and sentimentsInformation Fusion10.1016/j.inffus.2020.10.00367(29-40)Online publication date: Mar-2021

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