Paper Info
Title
Sentiment Analysis In Microblogs Using Hmms With Syntactic And Sentimental Information
Abstract
In this paper, we propose an approach for sentiment analysis in microblogs that learns patterns of syntactic and sentimental word transitions. Because sentences are sequences of words, we can more accurately analyze sentiments by properly modeling the sequential patterns of words in sentimental sentences. However, most previous research has focused on just extending feature sets using n-grams, POS tags, polarity lexicons, etc., without considering sequential patterns. Our proposed approach first identifies groups of words that have similar syntactic and sentimental roles, called SIGs (similar syntactic and sentimental information groups). We then build HMMs using the SIGs as hidden states for the initialization. The SIGs function as the prior knowledge of formative elements of sentimental sentences for HMMs. By using the SIGs, HMMs can start with informative hidden states and more precisely model the transition patterns of words in sentimental sentences with robust probability estimation. For the performance evaluation, we compare the proposed approach with existing ones using HCR dataset. The result shows that the proposed approach outperforms the previous ones in various performance measures.
Year
DOI
Venue
2017
10.5391/IJFIS.2017.17.4.329
INTERNATIONAL JOURNAL OF FUZZY LOGIC AND INTELLIGENT SYSTEMS
Keywords
Field
DocType
Sentiment analysis, Hidden Markov models, Gaussian mixture models, Syntactic and sentimental information
Social media,Sentiment analysis,Computer science,Microblogging,Artificial intelligence,Natural language processing,Hidden Markov model,Syntax,Mixture model
Journal
Volume
Issue
ISSN
17
4
1598-2645
Citations 
PageRank 
References 
0
0.34
2
Authors
3
Name
Order
Citations
PageRank
Noo-ri Kim1274.55
Kyoungmin Kim201.69
Jee-Hyong Lee331649.65