A hybrid model of sentimental entity recognition on mobile social media

With new forms of media such as Twitter becoming increasingly popular, the Internet is now the main conduit of individual and interpersonal messages. A considerable amount of people express their personal opinions about news-related subject through Twitter, a popular SNS platform based on human relationships. It provides us a data source that we can use to extract peoples’ opinions which are important for product review and public opinion monitoring. In this paper, a hybrid sentimental entity recognition model (HSERM) has been designed. Utilizing 100 million collected messages from Twitter, the hashtag is regarded as the label for sentimental classification. In the meanwhile, features as emoji and N-grams have been extracted and classified the collected topic messages into four different sentiment categories based on the circumplex sentimental model. Finally, machine learning methods are used to classify the sentimental data set, and an 89 % precise result has been achieved. Further, entities that are behind emotions could be gotten with the help of SENNA deep learning model.

[1]  Dilek Küçük,et al.  Experiments to Improve Named Entity Recognition on Turkish Tweets , 2014, ArXiv.

[2]  Dilek Küçük,et al.  Named Entity Recognition on Turkish Tweets , 2014, LREC.

[3]  R. Pavitra,et al.  Weakly supervised sentiment analysis using joint sentiment topic detection with bigrams , 2015, 2015 2nd International Conference on Electronics and Communication Systems (ICECS).

[4]  Ying Yu,et al.  Key Technology Research on User Identity Resolution across Multi-social Media , 2015, 2015 International Conference on Cloud Computing and Big Data (CCBD).

[5]  Shaowen Wang,et al.  Exploiting Feature Selection Algorithm on Group Events Data Based on News Data , 2015, 2015 International Conference on Identification, Information, and Knowledge in the Internet of Things (IIKI).

[6]  Vaibhavi N Patodkar,et al.  Twitter as a Corpus for Sentiment Analysis and Opinion Mining , 2016 .

[7]  Behrang Mohit,et al.  Named Entity Recognition , 2014, NLP of Semitic Languages.

[8]  Jonathon Read,et al.  Using Emoticons to Reduce Dependency in Machine Learning Techniques for Sentiment Classification , 2005, ACL.

[9]  Cheng Hu,et al.  Chinese Social Media Analysis for Disease Surveillance , 2014, IIKI.

[10]  Huang De.  Identification of Chinese Names Based on Statistics , 2001 .

[11]  Raphaël Troncy,et al.  Analysis of named entity recognition and linking for tweets , 2014, Inf. Process. Manag..

[12]  Yuji Matsumoto,et al.  Chinese Unknown Word Identification Using Character-based Tagging and Chunking , 2003, ACL.

[13]  Firoz Khan,et al.  Sentiment Analysis of Twitter Data , 2018, International Research Journal on Advanced Science Hub.