Word Research on Sentiment Analysis of Foreign News Reports Concerning China Based on the Hybrid Model of Opinion Sentence and BLSTM-Attention

2021 ◽  
pp. 351-360
Author(s):  
Xiaohang Xu ◽  
Guowei Chen
Keyword(s):  
Sentiment Analysis ◽  
Hybrid Model ◽  
News Reports ◽  
Foreign News

scholarly journals KNN classifier based approach for multi-class sentiment analysis of twitter data

2018 ◽  
Vol 7 (3) ◽  
pp. 1372
Author(s):  
Soudamini Hota ◽  
Sudhir Pathak
Keyword(s):  
Sentiment Analysis ◽  
Opinion Mining ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Online News ◽  
Classification Algorithm ◽  
Sentiment Classification ◽  
Supervised Machine Learning ◽  
K Nearest Neighbor ◽  
News Reports

‘Sentiment’ literally means ‘Emotions’. Sentiment analysis, synonymous to opinion mining, is a type of data mining that refers to the analy-sis of data obtained from microblogging sites, social media updates, online news reports, user reviews etc., in order to study the sentiments of the people towards an event, organization, product, brand, person etc. In this work, sentiment classification is done into multiple classes. The proposed methodology based on KNN classification algorithm shows an improvement over one of the existing methodologies which is based on SVM classification algorithm. The data used for analysis has been taken from Twitter, this being the most popular microblogging site. The source data has been extracted from Twitter using Python’s Tweepy. N-Gram modeling technique has been used for feature extraction and the supervised machine learning algorithm k-nearest neighbor has been used for sentiment classification. The performance of proposed and existing techniques is compared in terms of accuracy, precision and recall. It is analyzed and concluded that the proposed technique performs better in terms of all the standard evaluation parameters. 

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

scholarly journals Social Actor Representation of the Missing Malaysia Airlines Flight MH370 in the Malaysian and Foreign News Reports: A Critical Discourse Analysis

2019 ◽  
Vol 8 (2) ◽  
pp. 188
Author(s):  
Arlini binti Alias ◽  
Nora Mohd Nasir
Keyword(s):  
New York ◽  
Critical Discourse Analysis ◽  
New York Times ◽  
Intergroup Bias ◽  
The Other ◽  
Social Actor ◽  
Daily Mail ◽  
Social Actors ◽  
News Reports ◽  
Foreign News

The objective of this study is to examine the linguistic representation of social actors in the selected Malaysian and foreign news reports on the circulated event of the missing MAS flight MH370. Despite extensive studies of news discourse, less attention is paid on how news event are speculated and the extent the social actors are relegated. Hence, the study explores the role of newspaper editorials in promoting stereotypical depictions through the representation of self- and other- in their reporting of the MH370 tragedy. The study retrieved a total of fifty (50) news reports of the missing MAS flight MH370 incident from ten news press, twenty-five (25) published by five local (Malaysian) English newsagents: The Star, New Straits Times, Sun Daily, Malaysian Insider and Malaysiakini, and twenty-five (25) others from five foreign newsagents: Daily Mail (UK), The Guardian (UK), Washington Post, New York Times and USA Today. The corpora were collected from March 8, 2014, to November 5, 2014, and analysed using Van Dijk’s (1998) Ideological Square framework, as well as Reisigl and Wodak (2000) Discursive Strategies. The analysis of this study discovers evidence of the “intergroup bias” made by the selected news press in representing the MH370 social actors. The selected news press displays an overt preference for own group and obvious demotion of the other group. The study also reveals the occurrence of lexicalization of the ‘other’ in the foreign news reports indicating positive representation of their in-group and exhibiting apparent disapproval of the actions by the out-group. On the other hand, the analysis also reveals an impartial representation of the MH370 social actor by the local news press both for in-group and out-group.

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