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.
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〉
https://hal.inria.fr/hal-01656232
Contributeur : Hal Ifip
<>
Soumis le : mardi 5 décembre 2017 - 14:58:02
Dernière modification le : mercredi 6 décembre 2017 - 01:20:58
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〉
