Intermodal Sentiment Analysis for Images with Text Captions Using the VGGNET Technique

2021 ◽  
Vol 20 (4) ◽  
pp. 1-14
Author(s):  
R. Sathish ◽  
P. Ezhumalai
Keyword(s):  
Sentiment Analysis ◽  
High Accuracy ◽  
Research Trend ◽  
Social Involvement ◽  
Analysis Model ◽  
Visual Content ◽  
Simulation Experiments ◽  
Low Delay ◽  
Mapping Process ◽  
Text Sentiment Analysis

More individuals actively express their opinions and attitudes in social media through advanced improvements such as visual content and text captions. Sentiment analysis for visuals such as images, video, and GIFs has become an emerging research trend in understanding social involvement and opinion prediction. Numerous individual researchers have obtained good progress in outcomes for text sentiment analysis and image sentiment analysis. The combination of image sentiment analysis with text caption analysis needs more research. This article presents a VGG Network-based Intermodal Sentiment Analysis Model (VGGNET-ISAM) for transferring the connection between texts to images. A mapping process is developed using the VGG Network for gathering the opinion information as numerical vectors. The Active Deep Learning (ADL) classifier is used for opinion prediction from the obtained information vectors. Simulation experiments are carried out to evaluate the proposed approach. The findings show that the model outperforms and gives better solutions with high accuracy, precision with low delay, and low error rate.

scholarly journals Sentiment Analysis Method based on Piecewise Convolutional Neural Network and Generative Adversarial Network

2019 ◽  
Vol 14 (1) ◽  
pp. 7-20 ◽  
Author(s):  
Changshun Du ◽  
Lei Huang
Keyword(s):  
Neural Network ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Convolution Neural Network ◽  
Training Data ◽  
Sentence Structure ◽  
Analysis Model ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Text Sentiment Analysis

Text sentiment analysis is one of the most important tasks in the field of public opinion monitoring, service evaluation and satisfaction analysis under network environments. Compared with the traditional Natural Language Processing analysis tools, convolution neural networks can automatically learn useful features from sentences and improve the performance of the affective analysis model. However, the original convolution neural network model ignores sentence structure information which is very important for text sentiment analysis. In this paper, we add piece-wise pooling to the convolution neural network, which allows the model to obtain the sentence structure. And the main features of different sentences are extracted to analyze the emotional tendencies of the text. At the same time, the user’s feedback involves many different fields, and there is less labeled data. In order to alleviate the sparsity of the data, this paper also uses the generative adversarial network to make common feature extractions, so that the model can obtain the common features associated with emotions in different fields, and improves the model’s Generalization ability with less training data. Experiments on different datasets demonstrate the effectiveness of this method.

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

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.

An ANEW based Fuzzy Sentiment Analysis Model

2018 ◽  
Author(s):  
Andres Montoro ◽  
Jose A. Olivas ◽  
Arturo Peralta ◽  
Francisco P. Romero ◽  
Jesus Serrano-Guerrero
Keyword(s):  
Sentiment Analysis ◽  
Analysis Model
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