International Journal of Computational Linguistics & Chinese Language Processing, Volume 23, Number 1, June 2018
7478Authors
nan
Abstract
In this paper, we describe a sentiment analysis system implemented for the semantic-evaluation task of message polarity classification for English on Twitter. Our system contains modules of data pre-processing, word embedding, and sentiment classification. In order to decrease the data complexity and increase the coverage of the word vector model for better learning, we perform a series of data pre-processing tasks, including emoticon normalization, specific suffix splitting, and hashtag segmentation. In word embedding, we utilize the pre-trained word vector provided by GloVe. We believe that emojis in tweets are important characteristics for Twitter sentiment classification, but most pre-trained sets of word vectors contain few or no emoji representations. Thus, we propose embedding emojis into the vector space by neural network models. We train the emoji vector with relevant words that contain descriptions and contexts of emojis. The models of long short-term memory (LSTM) and convolutional neural network (CNN) are used as our sentiment classifiers. The proposed emoji embedding is evaluated on the SemEval 2017 tasks. Using emoji embedding, we achieved recall rates of 0.652 with the LSTM classifier and 0.640 with the CNN classifier. Keywords: Sentiment Analysis, Polarity Classification, Machine Learning, Neural Network, Word Embedding. 1.
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International Journal of Computational Linguistics & Chinese Language Processing Aims and Scope International Journal of Computational Linguistics and Chinese Language Processing (IJCLCLP) is an international journal published by the Association for Computational Linguistics and Chinese Language Processing (ACLCLP). This journal was founded in August 1996 and is published four issues per year since 2005. This journal covers all aspects related to computational linguistics and speech/text processing of all natural languages. Possible topics for manuscript submitted to the journal include, but are not limited to: • Computational Linguistics • Natural Language Processing • Machine Translation • Language Generation • Language Learning • Speech Analysis/Synthesis • Speech Recognition/Understanding • Spoken Dialog Systems • Information Retrieval and Extraction • Web Information Extraction/Mining • Corpus Linguistics • Multilingual/Cross-lingual Language Processing Membership & Subscriptions If you are interested in joining ACLCLP, please see appendix for further information. Copyright © The Association for Computational Linguistics and Chinese Language Processing International Journal of Computational Linguistics and Chinese Language Processing is published four issues per volume by the Association for Computational Linguistics and Chinese Language Processing. Responsibility for the contents rests upon the authors and not upon ACLCLP, or its members. Copyright by the Association for Computational Linguistics and Chinese Language Processing. All rights reserved. No part of this journal may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical photocopying, recording or otherwise, without prior permission in writing form from the Editor-in Chief. Cover Calligraphy by Professor Ching-Chun Hsieh, founding president of ACLCLP Text excerpted and compiled from ancient Chinese classics, dating back to 700 B.C. This calligraphy honors the interaction and influence between text and language International Journal of Computational Linguistics and Chinese Language Processing vol. 23, no. 1, June 2018 Contents Papers Sentiment Analysis on Social Network: Using Emoticon Characteristics for Twitter Polarity Classification………………... Chia-Ping Chen, Tzu-Hsuan Tseng and Tzu-Hsuan Yang 1 長短期記憶模型之忘記閘提取語意流暢度之架構以自閉症小 孩說故事為例 [A Lexical Coherence Representation Computational Framework using LSTM Forget Gate For Autism Recognition]………………………………………………….…... 劉于碩(Yu-Shuo Liu),陳金博(Chin-Po Chen), 高淑芬(Susan Shur-Fen Gau),李祈均(Chi-Chun Lee) 19 整合個人化磁振造影深度神經網路之演算法技術 [Joint Modeling of Individual Neural Responses using a Deep Voting Fusion Network for Automatic Emotion Perception Decoding]..... 謝宛庭(Wan-Ting Hsieh),李祈均(Chi-Chun Lee) 35 The Association for Computational Linguistics and Chinese Language Processing Computational Linguistics and Chinese Language Processing Vol. 23, No. 1, June 2018, pp. 1-18 1 The Association for Computational Linguistics and Chinese Language Processing Sentiment Analysis on Social Network: Using Emoticon Characteristics for Twitter Polarity Classification Chia-Ping Chen, Tzu-Hsuan Tseng and Tzu-Hsuan Yang Abstract In this paper, we describe a sentiment analysis system implemented for the semantic-evaluation task of message polarity classification for English on Twitter. Our system contains modules of data pre-processing, word embedding, and sentiment classification. In order to decrease the data complexity and increase the coverage of the word vector model for better learning, we perform a series of data pre-processing tasks, including emoticon normalization, specific suffix splitting, and hashtag segmentation. In word embedding, we utilize the pre-trained word vector provided by GloVe. We believe that …
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In this paper, we describe a sentiment analysis system implemented for the semantic-evaluation task of message polarity classification for English on Twitter. Our system contains modules of data pre-processing, word embedding, and sentiment classification. In order to decrease the data complexity and increase the coverage of the word vector model for better learning, we perform a series of data pre-processing tasks, including emoticon normalization, specific suffix splitting, and hashtag segmentation. In word embedding, we utilize the pre-trained word vector provided by GloVe. We believe that emojis in tweets are important characteristics for Twitter sentiment classification, but most pre-trained sets of word vectors contain few or no emoji representations. Thus, we propose embedding emojis into the vector space by neural network models. We train the emoji vector with relevant words that contain descriptions and contexts of emojis. The models of long short-term memory (LSTM) and convolutional neural network (CNN) are used as our sentiment classifiers. The proposed emoji embedding is evaluated on the SemEval 2017 tasks. Using emoji embedding, we achieved recall rates of 0.652 with the LSTM classifier and 0.640 with the CNN classifier. Keywords: Sentiment Analysis, Polarity Classification, Machine Learning, Neural Network, Word Embedding. 1.
Publication Details
- Paper ID:
- 7478
- Year:
- 2018
- Publication:
- TIPSTER
- Publication Type:
- conf
- DOI:
- nan