Current State of Text Sentiment Analysis from Opinion to Emotion Mining

2017 ◽  
Vol 50 (2) ◽  
pp. 1-33 ◽  
Author(s):  
Ali Yadollahi ◽  
Ameneh Gholipour Shahraki ◽  
Osmar R. Zaiane
Keyword(s):  
Sentiment Analysis ◽  
Current State ◽  
Text Sentiment Analysis ◽  
Emotion Mining

Current State of Hinglish Text Sentiment Analysis

2020 ◽  
Author(s):  
Varsha Thakur ◽  
Roshani Sahu ◽  
Somya Omer
Keyword(s):  
Sentiment Analysis ◽  
Current State ◽  
Text Sentiment Analysis

Text sentiment analysis-oriented commodity review detection

2013 ◽  
Vol 32 (7) ◽  
pp. 2038-2040 ◽  
Author(s):  
Hai-jie PANG
Keyword(s):  
Sentiment Analysis ◽  
Text Sentiment Analysis

Survey of text sentiment analysis

2013 ◽  
Vol 33 (6) ◽  
pp. 1574-1578 ◽  
Author(s):  
Ligong YANG ◽  
Jian ZHU ◽  
Shiping TANG
Keyword(s):  
Sentiment Analysis ◽  
Text Sentiment Analysis

scholarly journals UTSA: Urdu Text Sentiment Analysis Using Deep Learning Methods

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Uzma Naqvi ◽  
Abdul Majid ◽  
S. Ali Abbas
Keyword(s):  
Deep Learning ◽  
Sentiment Analysis ◽  
Learning Methods ◽  
Text Sentiment Analysis

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.

scholarly journals Sentiment Analysis Method of Network Text Based on Improved AT-BiGRU Model

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Xinxin Lu ◽  
Hong Zhang
Keyword(s):  
Sentiment Analysis ◽  
Current Method ◽  
Recurrent Network ◽  
Analysis Method ◽  
Large Space ◽  
Spelling Errors ◽  
Training Time ◽  
Proposed Model ◽  
Correct Spelling ◽  
Text Sentiment Analysis

In order to solve the problems existing in the current method of emotional analysis of network text, such as long training time, complex calculation, and large space cost, this paper proposes an Internet text sentiment analysis method based on the improved AT-BiGRU model. Firstly, the textblob package is imported to correct spelling errors before text preprocessing. Secondly, pad_sequences are used to fill in the input layer with a fixed length, the two-way gated recurrent network is used to extract information, and the attention mechanism is used to highlight the key information of the word vector. Finally, the GNU memory unit is transformed, and an improved BiGRU that can adapt to the recursive network structure is constructed. The proposed model is experimentally demonstrated on the SemEval-2014 Task 4 and SemEval-2017 Task 4 datasets. Experimental results show that the proposed model can effectively avoid the text sentiment analysis bias caused by spelling errors and prove the effectiveness of the improved AT-BiGRU model in terms of accuracy, loss rate, and iteration time.

scholarly journals TEXT SENTIMENT ANALYSIS BASED ON CNNS AND SVM

2019 ◽  
Vol 7 (6) ◽  
pp. 77-83 ◽  
Author(s):  
Dr. C. Arunabala ◽  
P. Jwalitha ◽  
Soniya Nuthalapati
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Sentiment Analysis ◽  
Expressive Power ◽  
Sentiment Classification ◽  
Experimental Results ◽  
Analysis Method ◽  
Mapping Functions ◽  
Generalization Ability ◽  
Text Sentiment Analysis

The traditional text sentiment analysis method is mainly based on machine learning. However, its dependence on emotion dictionary construction and artificial design and extraction features makes the generalization ability limited. In contrast, depth models have more powerful expressive power, and can learn complex mapping functions from data to affective semantics better. In this paper, a Convolution Neural Networks (CNNs) model combined with SVM text sentiment analysis is proposed. The experimental results show that the proposed method improves the accuracy of text sentiment classification effectively compared with traditional CNN, and confirms the effectiveness of sentiment analysis based on CNNs and SVM

scholarly journals Sentiment of App with Word Vectors

2019 ◽  
Vol 8 (6S3) ◽  
pp. 2156-2159
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Text Data ◽  
Vector Representations ◽  
Text Sentiment Analysis

Vector representations for language have been shown to be useful in a number of Natural Language Processing tasks. In this paper, we aim to investigate the effectiveness of word vector representations for the problem of Sentiment Analysis. In particular, we target three sub-tasks namely sentiment words extraction, polarity of sentiment words detection, and text sentiment prediction. We investigate the effectiveness of vector representations over different text data and evaluate the quality of domain-dependent vectors. Vector representations has been used to compute various vector-based features and conduct systematically experiments to demonstrate their effectiveness. Using simple vector based features can achieve better results for text sentiment analysis of APP.

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