You Are What You Tweet: A New Hybrid Model for Sentiment Analysis

2017 ◽  
pp. 403-416 ◽  
Author(s):  
Arthur Huang ◽  
David Ebert ◽  
Parker Rider
Keyword(s):  
Sentiment Analysis ◽  
Hybrid Model

A New Semantic Relations-Based Hybrid Approach for Implicit Aspect Identification in Sentiment Analysis

2020 ◽  
Vol 19 (03) ◽  
pp. 2050019
Author(s):  
Hajar El Hannach ◽  
Mohammed Benkhalifa
Keyword(s):  
Sentiment Analysis ◽  
Hybrid Model ◽  
Opinion Mining ◽  
Short Term Memory ◽  
Hybrid Approach ◽  
Active Phase ◽  
Semantic Relations ◽  
Term Extraction ◽  
Supervised Classifiers ◽  
Restaurant Reviews

Within the next few years, sentiment analysis or opinion mining is set to become an important component of real-world applications for product manufacturers, e-commerce companies, and potential customers. Sentiment analysis deals with the computational assessment of people’s opinions apparent or hidden within the text according to three levels: document, sentence and aspect levels. The aspect-level is increasingly becoming an active phase of sentiment analysis. At this level, the aim is to determine the hidden target of opinion represented in datasets, known as aspect term identification. This paper proposes an original hybrid model combining semantic relations and frequency-based approach with supervised classifiers for implicit aspect identification (IAI). The proposed approach is directed towards improving the F1-performances for traditional supervised classifiers commonly used in this field based on eager and lazy learning, and deep learning technique using long short-term memory whit attention mechanism applied for IAI. Particularly, this work addresses aspect term extraction and aggregation, the two sub-tasks of IAI, involving adjectives and verbs. The effects of this approach are empirically examined on multiple datasets of electronic products and restaurant reviews with multiple aspect granularity levels. Comparing this method with similar approaches clearly shows the benefits of this method: (i) the use of an appropriately selected WordNet semantic relations of adjectives and verbs that significantly helps classifiers for IAI. (ii) Using the hybrid model helps classifiers better handle these selected WordNet semantic relations and therefore deal better with IAI.

An effective hybrid model for opinion mining and sentiment analysis

2017 ◽  
Author(s):  
Kai Yang ◽  
Yi Cai ◽  
Dongping Huang ◽  
Jingnan Li ◽  
Zikai Zhou ◽  
...  
Keyword(s):  
Sentiment Analysis ◽  
Hybrid Model ◽  
Opinion Mining

An Efficient Hybrid Model for Vietnamese Sentiment Analysis

2017 ◽  
pp. 227-237
Author(s):  
Thanh Hung Vo ◽  
Thien Tin Nguyen ◽  
Hoang Anh Pham ◽  
Thanh Van Le
Keyword(s):  
Sentiment Analysis ◽  
Hybrid Model
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