Public Opinion Prediction by Combining Link Analysis and Sentiment Detection
碩士 === 國立臺北科技大學 === 資訊工程系研究所 === 105 === With the popularity of social network and mobile network, people can easily share opinion with others in social networking site. More and more research tends to analyze user data on the website rather than use traditional methods. By researching user data on...
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ndltd-TW-105TIT053920202019-05-15T23:53:23Z http://ndltd.ncl.edu.tw/handle/7jm328 Public Opinion Prediction by Combining Link Analysis and Sentiment Detection 結合連結分析與情緒偵測之民意預測研究 Ting-Wei Liu 劉庭瑋 碩士 國立臺北科技大學 資訊工程系研究所 105 With the popularity of social network and mobile network, people can easily share opinion with others in social networking site. More and more research tends to analyze user data on the website rather than use traditional methods. By researching user data on the website, we can improve the accuracy of prediction because of the large number of data. In order to improve the accuracy of opinion prediction. We combined sentiment detection and link analysis in this paper. In terms of sentiment detection, we use Long Short-Term Memory (LSTM) networks to train the sentiment classifier, and predict user sentiments in the posts by hierarchical method. In terms of link analysis, we calculate influence score of users and posts by TURank. We also change the link in TUrank, solve the memory problem of computing by observe social networking site. At the end, we predict user opinion by sentiment detection result, and then use influence score as user weight to calculate opinion polarity score. We use the result of 2016 United States presidential election to evaluate our method. Results shows our classier have a high F-measure of 72.3%, and can effectively predict the election result by combining influence score. Jenq-Haur Wang 王正豪 2017 學位論文 ; thesis 55 zh-TW |
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碩士 === 國立臺北科技大學 === 資訊工程系研究所 === 105 === With the popularity of social network and mobile network, people can easily share opinion with others in social networking site. More and more research tends to analyze user data on the website rather than use traditional methods. By researching user data on the website, we can improve the accuracy of prediction because of the large number of data.
In order to improve the accuracy of opinion prediction. We combined sentiment detection and link analysis in this paper. In terms of sentiment detection, we use Long Short-Term Memory (LSTM) networks to train the sentiment classifier, and predict user sentiments in the posts by hierarchical method. In terms of link analysis, we calculate influence score of users and posts by TURank. We also change the link in TUrank, solve the memory problem of computing by observe social networking site. At the end, we predict user opinion by sentiment detection result, and then use influence score as user weight to calculate opinion polarity score.
We use the result of 2016 United States presidential election to evaluate our method. Results shows our classier have a high F-measure of 72.3%, and can effectively predict the election result by combining influence score.
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author2 |
Jenq-Haur Wang |
author_facet |
Jenq-Haur Wang Ting-Wei Liu 劉庭瑋 |
author |
Ting-Wei Liu 劉庭瑋 |
spellingShingle |
Ting-Wei Liu 劉庭瑋 Public Opinion Prediction by Combining Link Analysis and Sentiment Detection |
author_sort |
Ting-Wei Liu |
title |
Public Opinion Prediction by Combining Link Analysis and Sentiment Detection |
title_short |
Public Opinion Prediction by Combining Link Analysis and Sentiment Detection |
title_full |
Public Opinion Prediction by Combining Link Analysis and Sentiment Detection |
title_fullStr |
Public Opinion Prediction by Combining Link Analysis and Sentiment Detection |
title_full_unstemmed |
Public Opinion Prediction by Combining Link Analysis and Sentiment Detection |
title_sort |
public opinion prediction by combining link analysis and sentiment detection |
publishDate |
2017 |
url |
http://ndltd.ncl.edu.tw/handle/7jm328 |
work_keys_str_mv |
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