Ontology-based Aspect Extraction for an Improved Sentiment Analysis in Summarization of Product Reviews

2017 ◽  
Author(s):  
Ali Marstawi ◽  
Nurfadhlina Mohd Sharef ◽  
Teh Noranis Mohd Aris ◽  
Aida Mustapha
Keyword(s):  
Sentiment Analysis ◽  
Product Reviews ◽  
Aspect Extraction

scholarly journals RoBERTa-IAN for aspect-level sentiment analysis of product reviews

2021 ◽  
Vol 1827 (1) ◽  
pp. 012079
Author(s):  
Jingrui Dai ◽  
Fang Pan ◽  
Zhaoyu Shou ◽  
Huibing Zhang
Keyword(s):  
Sentiment Analysis ◽  
Product Reviews

scholarly journals Knowing What, How and Why: A Near Complete Solution for Aspect-Based Sentiment Analysis

2020 ◽  
Vol 34 (05) ◽  
pp. 8600-8607
Author(s):  
Haiyun Peng ◽  
Lu Xu ◽  
Lidong Bing ◽  
Fei Huang ◽  
Wei Lu ◽  
...  
Keyword(s):  
Sentiment Analysis ◽  
State Of The Art ◽  
Complete Solution ◽  
Unified Model ◽  
Two Stage ◽  
Fine Grained ◽  
Aspect Extraction ◽  
Second Stage ◽  
Opinion Extraction ◽  
Complete Story

Target-based sentiment analysis or aspect-based sentiment analysis (ABSA) refers to addressing various sentiment analysis tasks at a fine-grained level, which includes but is not limited to aspect extraction, aspect sentiment classification, and opinion extraction. There exist many solvers of the above individual subtasks or a combination of two subtasks, and they can work together to tell a complete story, i.e. the discussed aspect, the sentiment on it, and the cause of the sentiment. However, no previous ABSA research tried to provide a complete solution in one shot. In this paper, we introduce a new subtask under ABSA, named aspect sentiment triplet extraction (ASTE). Particularly, a solver of this task needs to extract triplets (What, How, Why) from the inputs, which show WHAT the targeted aspects are, HOW their sentiment polarities are and WHY they have such polarities (i.e. opinion reasons). For instance, one triplet from “Waiters are very friendly and the pasta is simply average” could be (‘Waiters’, positive, ‘friendly’). We propose a two-stage framework to address this task. The first stage predicts what, how and why in a unified model, and then the second stage pairs up the predicted what (how) and why from the first stage to output triplets. In the experiments, our framework has set a benchmark performance in this novel triplet extraction task. Meanwhile, it outperforms a few strong baselines adapted from state-of-the-art related methods.

Sentiment Analysis of Kaspi Product Reviews

2021 ◽  
Author(s):  
Yerassyl Kelsingazin ◽  
Iskander Akhmetov ◽  
Alexander Pak
Keyword(s):  
Sentiment Analysis ◽  
Product Reviews

TASKS AND METHODS OF TEXT SENTIMENT ANALYSIS

2021 ◽  
pp. 55-62
Author(s):  
А. Mukasheva
Keyword(s):  
Social Networks ◽  
Sentiment Analysis ◽  
State Level ◽  
Video Object ◽  
Aspect Extraction ◽  
Networks Analysis ◽  
As Graph ◽  
Definition Of ◽  
Unstructured Information ◽  
Text Sentiment Analysis

The purpose of this article is to study one of the methods of social networks analysis – text sentiment analysis. Today, social media has become a big data base that social network analysis is used for various purposes – from setting up targeted advertising for a cosmetics store to preventing riots at the state level. There are various methods for analyzing social networks such as graph method, text sentiment analysis, audio, and video object analysis. Among them, sentiment analysis is widely used for political, social, consumer research, and also for cybersecurity. Since the analysis of the sentiment of the text involves the analysis of the emotional opinions expressed in the text, the first step is to define the term opinion. An opinion can be simple, that is, a positive, negative or neutral emotion towards a particular object or its aspect. Comparison is also an opinion, but devoid of emotional connotation. To work with simple opinions, the first task of text sentiment analysis is to classify the text. There are three levels of classifications: classification at the text level, at the level of a sentence, and at the aspect level of the object. After classifying the text at the desired level, the next task is to extract structured data from unstructured information. The problem can be solved using the five-tuple method. One of the important elements of a tuple is the aspect in which an opinion is usually expressed. Next, aspect-based sentiment analysis is applied, which involves identifying aspects of the desired object and assessing the polarity of mood for each aspect. This task is divided into two sub-tasks such as aspect extraction and aspect classification. Sentiment analysis has limitations such as the definition of sarcasm and difficulty of working with abbreviated words.

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