Towards the Exploitation of Statistical Language Models for Sentiment Analysis of Twitter Posts

Abstract : In this paper, we investigate the utility of linguistic features for detecting the sentiment of twitter messages. The sentiment is defined to be a personal positive or negative feelings. We built n-gram language models over zoos of positive and negative tweets. We assert the polarity of a given tweet by observing the perplexity with the positive or negative language model. The given tweet is considered to be close to the language model that assigns lower perplexity.
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Khalid Saeed; Władysław Homenda; Rituparna Chaki. 16th IFIP International Conference on Computer Information Systems and Industrial Management (CISIM), Jun 2017, Bialystok, Poland. Springer International Publishing, Lecture Notes in Computer Science, LNCS-10244, pp.253-263, 2017, Computer Information Systems and Industrial Management. 〈10.1007/978-3-319-59105-6_22〉
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Sukriti Bhattacharya, Prasun Banerjee. Towards the Exploitation of Statistical Language Models for Sentiment Analysis of Twitter Posts. Khalid Saeed; Władysław Homenda; Rituparna Chaki. 16th IFIP International Conference on Computer Information Systems and Industrial Management (CISIM), Jun 2017, Bialystok, Poland. Springer International Publishing, Lecture Notes in Computer Science, LNCS-10244, pp.253-263, 2017, Computer Information Systems and Industrial Management. 〈10.1007/978-3-319-59105-6_22〉. 〈hal-01656232〉

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