Sentiment analysis and opinions summrization on social media
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Sentiment analysis and opinions summrization on social media
SENTIMENT ANALYSIS AND OPINIONS SUMMARIZATION ON SOCIAL MEDIANGUYEN TIEN HUYJapan Advanced Institute of Science and Technologyhttps://khothu vien .com Sentiment analysis and opinions summrization on social media mDoctoral DissertationSENTIMENT ANALYSIS AND OPINIONS SUMMARIZATION ON SOCIAL MEDIANGUYEN TIEN HUYSupervisor : Associate Professor XGUYEN Le MinhGraduate School of Advanced Science and Technology Japan Advanced Institute of Science and Technology Information ScienceSeptember, 2019Copyright © 2019 by Sentiment analysis and opinions summrization on social media NGUYEN TIEN HUYDoctoral DissertationSENTIMENT ANALYSIS AND OPINIONS SUMMARIZATION ON SOCIAL MEDIANguyen Tien Huysubmitted toJapan Advanced InstituteSentiment analysis and opinions summrization on social media
of Science and Technology in partial fulfillment of the requirements for the degree ofDoctor of PhilosophyWritten under the direction of Associate ProSENTIMENT ANALYSIS AND OPINIONS SUMMARIZATION ON SOCIAL MEDIANGUYEN TIEN HUYJapan Advanced Institute of Science and Technologyhttps://khothu vien .com Sentiment analysis and opinions summrization on social media t of data. Platforms (e.g. Twitter, Facebook, and YouTube), which enable millions of users to share information and comments, have a high demand for extracting knowledge from user-generated content. Useful information to be analyzed from those comments are opinions/sentiments, which express subjecti Sentiment analysis and opinions summrization on social media ve opinions, ('Valuations, appraisals, attitudes, and emotions of particular users towards entities. If we can build a model to detect and summarize cSentiment analysis and opinions summrization on social media
orrectly and quickly opinions from comments of social media, we can extract/understand knowledge about the reputation of a person, organization or proSENTIMENT ANALYSIS AND OPINIONS SUMMARIZATION ON SOCIAL MEDIANGUYEN TIEN HUYJapan Advanced Institute of Science and Technologyhttps://khothu vien .com Sentiment analysis and opinions summrization on social media i) social media text covers a variety of domains (e.g., phone, education) that requires a robust approach against domains; iii) comments may not be related to topics or spams.The aim of this study is to obtain an effective method for identifying and summarizing opinions on social media. To this end, Sentiment analysis and opinions summrization on social media the research question is as follows: how to employ deep learning architectures to deal with the challenges of this task. As the advantages of deep leSentiment analysis and opinions summrization on social media
arning are to self-learn salient features from big data, we expect an efficient result from this approach for opinions summarization.To answer the resSENTIMENT ANALYSIS AND OPINIONS SUMMARIZATION ON SOCIAL MEDIANGUYEN TIEN HUYJapan Advanced Institute of Science and Technologyhttps://khothu vien .com Sentiment analysis and opinions summrization on social media f a comment/review. We propose a freezing technique to learn sentiment-specific vectors from CNN and LSTM. This teclmique is efficient for integrating the advantages of various deep learning models. We also observe that semantically clustering documents into groups is more beneficial for ensemble me Sentiment analysis and opinions summrization on social media thods.•Subject toward sentiment analysis: determines the target subject which the comment gives its sentiment to or the comment contains spam. We propSentiment analysis and opinions summrization on social media
ose a convolutional N-gram BiLSTM word embedding which represents a word with semantic and contextual information in short and long distance periods. SENTIMENT ANALYSIS AND OPINIONS SUMMARIZATION ON SOCIAL MEDIANGUYEN TIEN HUYJapan Advanced Institute of Science and Technologyhttps://khothu vien .com Sentiment analysis and opinions summrization on social media tic similarity q,j of two sentences i and j, which plays an important role in identifying the most informative sentences as well as redundant ones in summarization. We propose an M-MaxLS r.M-CNN model for employing multiple sets of word embeddings for evaluating sentence sim-ilarity/relation. Our mo Sentiment analysis and opinions summrization on social media del does not use hand-crafted features (e.g., alignment features, Ngram overlaps, dependency features) as well as does not require pre-trained word emSentiment analysis and opinions summrization on social media
beddings to have the same dimension.•Aspect similarity Recognition (ASR): identifies whether two sentences express one or some aspects in common. We pSENTIMENT ANALYSIS AND OPINIONS SUMMARIZATION ON SOCIAL MEDIANGUYEN TIEN HUYJapan Advanced Institute of Science and Technologyhttps://khothu vien .com Sentiment analysis and opinions summrization on social media id redundancy. To facilitate the application of supervised learning models for this task, we construct a dataset ASRCorpus containing two domains (i.e., LAPTOP and RESTAURANT). We propose an attention-cell LSTM model, which efficiently integrates attention signals into the I .STM gates.• Opinions Su Sentiment analysis and opinions summrization on social media mmarization: employs those signals above for ranking sentences. A concise and informative summary of a product e is generated by selecting rhe most saSentiment analysis and opinions summrization on social media
lient sentences from reviews. Applying ASR relaxes the constraint of predefined aspects in conventional aspect-based opinions summarization.According SENTIMENT ANALYSIS AND OPINIONS SUMMARIZATION ON SOCIAL MEDIANGUYEN TIEN HUYJapan Advanced Institute of Science and Technologyhttps://khothu vien .com Sentiment analysis and opinions summrization on social media ed Aspect Similarity Recognition subtask relaxes the limitation of predefining aspects and makes our opinions summarization applicable in domain adaptation. Further research could be undertaken to integrate transfer knowledge al sentence level as well as multitask learning for opinions summarization Sentiment analysis and opinions summrization on social media .Keywords: Sentiment Analysis. Opinion Mining. Opinions Summarization. Deep Learning, Aspect Similarity Recognition. Semantic Textual SiniilinityAcknoSentiment analysis and opinions summrization on social media
wledgmentsFirst of all. 1 wish to express my best sincerest gratitude to my principal advisor, Associate Professor Nguyen Le Minh of Japan Advanced InSENTIMENT ANALYSIS AND OPINIONS SUMMARIZATION ON SOCIAL MEDIANGUYEN TIEN HUYJapan Advanced Institute of Science and Technologyhttps://khothu vien .com Sentiment analysis and opinions summrization on social media me in researching as well as patiently taught me to be strong and self-confident in my study. Without his consistent support, I could not finish the work in this dissertationI would like to thank Professor Akira Shimazn, Professor Satoshi Tojo, Associate Professor Kiyoaki Shirai, Associate Professor Sentiment analysis and opinions summrization on social media Shinobu Hasegawa of .JA1ST. and Professor Ken Satoh of National Institute of Informatics for useful discussions and comments on this dissertation.SENTIMENT ANALYSIS AND OPINIONS SUMMARIZATION ON SOCIAL MEDIANGUYEN TIEN HUYJapan Advanced Institute of Science and Technologyhttps://khothu vien .comSENTIMENT ANALYSIS AND OPINIONS SUMMARIZATION ON SOCIAL MEDIANGUYEN TIEN HUYJapan Advanced Institute of Science and Technologyhttps://khothu vien .comGọi ngay
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