scholarly journals Bayesian estimation‐based sentiment word embedding model for sentiment analysis

2021 ◽  
Author(s):  
Jingyao Tang ◽  
Yun Xue ◽  
Ziwen Wang ◽  
Shaoyang Hu ◽  
Tao Gong ◽  
...  
Keyword(s):  
Sentiment Analysis ◽  
Bayesian Estimation ◽  
Word Embedding ◽  
Sentiment Word

GINS: A Global intensifier-based N-Gram sentiment dictionary

2021 ◽  
pp. 1-14
Author(s):  
Hamed Zargari ◽  
Morteza Zahedi ◽  
Marziea Rahimi
Keyword(s):  
Sentiment Analysis ◽  
Essential Elements ◽  
Analysis Methods ◽  
Sentiment Dictionary ◽  
N Gram ◽  
Heterogeneous Effect ◽  
Sentiment Word

Words are one of the most essential elements of expressing sentiments in context although they are not the only ones. Also, syntactic relationships between words, morphology, punctuation, and linguistic phenomena are influential. Merely considering the concept of words as isolated phenomena causes a lot of mistakes in sentiment analysis systems. So far, a large amount of research has been conducted on generating sentiment dictionaries containing only sentiment words. A number of these dictionaries have addressed the role of combinations of sentiment words, negators, and intensifiers, while almost none of them considered the heterogeneous effect of the occurrence of multiple linguistic phenomena in sentiment compounds. Regarding the weaknesses of the existing sentiment dictionaries, in addressing the heterogeneous effect of the occurrence of multiple intensifiers, this research presents a sentiment dictionary based on the analysis of sentiment compounds including sentiment words, negators, and intensifiers by considering the multiple intensifiers relative to the sentiment word and assigning a location-based coefficient to the intensifier, which increases the covered sentiment phrase in the dictionary, and enhanced efficiency of proposed dictionary-based sentiment analysis methods up to 7% compared to the latest methods.

A Comparative Study on Sentiment Analysis Influencing Word Embedding Using SVM and KNN

2021 ◽  
pp. 199-211
Author(s):  
Bachchu Paul ◽  
Sanchita Guchhait ◽  
Tanushree Dey ◽  
Debashri Das Adhikary ◽  
Somnath Bera
Keyword(s):  
Comparative Study ◽  
Sentiment Analysis ◽  
Word Embedding

Determine the Polarity of Domain-Specific Sentiment Words with Usage of Semantic Pattern of Sentences

2013 ◽  
Vol 347-350 ◽  
pp. 2340-2343
Author(s):  
Li Gong Yang ◽  
Bo Deng
Keyword(s):  
Sentiment Analysis ◽  
High Precision ◽  
Computational Linguistics ◽  
High Similarity ◽  
Domain Specific ◽  
Pattern Vector ◽  
Text Sentiment Analysis ◽  
Sentiment Word

Text sentiment analysis is a new branch of computational linguistics which is widely concerned. In this paper, we present an approach to determine polarity of sentiment word based on context of sentence. We first change the context of sentence to semantic pattern vector, calculate the between different sentences, then compare sentences context indirectly by comparing similarity of their pattern vector, next we annotate polarity of sentiment word according to comparing result. Experiment shows that when the context of two sentences have high similarity, it is likely to have high precision in recognizing polarity of sentiment word. Our study shows it's feasible to use semantic pattern vector in representing context and judging polarity of sentiment words.

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