scholarly journals Lexical Sense Labeling and Sentiment Potential Analysis Using Corpus-Based Dependency Graph

Mathematics ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 1449
Author(s):  
Tajana Ban Ban Kirigin ◽  
Sanda Bujačić Bujačić Babić ◽  
Benedikt Perak
Keyword(s):  
Sentiment Analysis ◽  
Dependency Graph ◽  
Conceptual Similarity ◽  
Knowledge Representations ◽  
Potential Analysis ◽  
Complementary Method ◽  
Propagation Methods ◽  
Graph Propagation ◽  
Semantic Dependency ◽  
Word Senses

This paper describes a graph method for labeling word senses and identifying lexical sentiment potential by integrating the corpus-based syntactic-semantic dependency graph layer, lexical semantic and sentiment dictionaries. The method, implemented as ConGraCNet application on different languages and corpora, projects a semantic function onto a particular syntactical dependency layer and constructs a seed lexeme graph with collocates of high conceptual similarity. The seed lexeme graph is clustered into subgraphs that reveal the polysemous semantic nature of a lexeme in a corpus. The construction of the WordNet hypernym graph provides a set of synset labels that generalize the senses for each lexical cluster. By integrating sentiment dictionaries, we introduce graph propagation methods for sentiment analysis. Original dictionary sentiment values are integrated into ConGraCNet lexical graph to compute sentiment values of node lexemes and lexical clusters, and identify the sentiment potential of lexemes with respect to a corpus. The method can be used to resolve sparseness of sentiment dictionaries and enrich the sentiment evaluation of lexical structures in sentiment dictionaries by revealing the relative sentiment potential of polysemous lexemes with respect to a specific corpus. The proposed approach has the potential to be used as a complementary method to other NLP resources and tasks, including word disambiguation, domain relatedness, sense structure, metaphoricity, as well as a cross- and intra-cultural discourse variations of prototypical conceptualization patterns and knowledge representations.

scholarly journals Creating Brand Image Profile by Social Media Analysis

2021 ◽  
Vol 11 (2) ◽  
pp. 8-15
Author(s):  
İbrahim Sabuncu ◽  
Berivan Edeş ◽  
Doruk Sıtkıbütün ◽  
İlayda Girgin ◽  
Kadir Zehir
Keyword(s):  
Artificial Intelligence ◽  
Social Media ◽  
Sentiment Analysis ◽  
Brand Image ◽  
Media Analysis ◽  
Alternative Methods ◽  
Social Media Analysis ◽  
Focus Group Study ◽  
Complementary Method ◽  
Mining Tool

The purpose of creating a brand image profile is to measure the brand perception of consumers considering brand attributes. Thus, marketing decisions can be made based on the brand's strengths and weaknesses by determining them. The brand image profile is traditionally created using the attitude scales and surveys. However, alternative methods are needed since the questionnaires' responses are careless, the number of participants is relatively low and the cost per participant is high. In this study, as an alternative method, creating a brand image profile by analyzing social media data with artificial intelligence was made for the iPhone product. Firstly, the focus group study determined the attributes related to the last version of the iPhone. Then, between December 17th, 2019 and March 23rd, 2020, 87.227 tweets that include these attributes in English were collected from the Twitter social media platform through the RapidMiner data mining tool. Sentiment analysis was performed on collected tweets by the MeaningCloud text mining tool. In this analysis, positive and negative emotions were tried to be detected through artificial intelligence algorithms. Net Brand Reputation Score (NBR) was calculated using the positive and negative tweets amount for each attribute separately. Brand image profile was created by skew analysis using NBR values. As a result, it is thought that social media analysis can be a complementary method that can be used with traditional methods in creating a brand image profile. So, it is seen as an inevitable method to use in further studies to make sentiment analysis by processing raw data received from the Social Media platforms through artificial intelligence algorithms to transform the product label or the perspectives of an event into meaningful information.

Relation extraction based on semantic dependency graph

2020 ◽  
Vol 20 (1) ◽  
pp. 279-290
Author(s):  
Ming Jiang ◽  
Jiecheng He ◽  
Jianping Wu ◽  
Chengjie Qi ◽  
Min Zhang
Keyword(s):  
Relation Extraction ◽  
Dependency Graph ◽  
Semantic Dependency

Aspect-based Sentiment Analysis using Dependency Parsing

2022 ◽  
Vol 21 (3) ◽  
pp. 1-19
Author(s):  
Sujata Rani ◽  
Parteek Kumar
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Minimum Distance ◽  
Dependency Graph ◽  
Media Analysis ◽  
Product Reviews ◽  
Dependency Parsing ◽  
Social Media Analysis ◽  
Dependency Parser ◽  
Sentiment Word

In this paper, an aspect-based Sentiment Analysis (SA) system for Hindi is presented. The proposed system assigns a separate sentiment towards the different aspects of a sentence as well as it evaluates the overall sentiment expressed in a sentence. In this work, Hindi Dependency Parser (HDP) is used to determine the association between an aspect word and a sentiment word (using Hindi SentiWordNet) and works on the idea that closely connected words come together to express a sentiment about a certain aspect. By generating a dependency graph, the system assigns the sentiment to an aspect having a minimum distance between them and computes the overall polarity of the sentence. The system achieves an accuracy of 83.2% on a corpus of movie reviews and its results are compared with baselines as well as existing works on SA. From the results, it has been observed that the proposed system has the potential to be used in emerging applications like SA of product reviews, social media analysis, etc.

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