A Hybrid Multilingual Fuzzy-Based Approach to the Sentiment Analysis Problem Using SentiWordNet

2020 ◽  
Vol 28 (03) ◽  
pp. 361-390
Author(s):  
Youness Madani ◽  
Mohammed Erritali ◽  
Jamaa Bengourram ◽  
Francoise Sailhan
Keyword(s):  
Fuzzy Logic ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Opinion Mining ◽  
Hybrid Approach ◽  
Research Area ◽  
Hadoop Mapreduce ◽  
Hadoop Distributed File System ◽  
Active Research ◽  
New Research

Sentiment Analysis or in particular social network analysis (SNA) is a new research area which is increased explosively. This domain has become a very active research issue in data mining and natural language processing. Sentiment analysis (opinion mining) consists in analyzing and extracting emotions, opinions or attitudes from product’s reviews, movie's reviews, etc., and classify them into classes such as positive, negative and neutral, or extract the degree of importance (polarity). In this paper, we propose a new hybrid approach for classifying tweets into classes based on fuzzy logic and a lexicon based approach using SentiWordnet. Our approach consists in classifying tweets according to three classes: positive, negative or neutral, using SentiWordNet and the fuzzy logic with its three important steps: Fuzzification, Rule Inference/aggregation, and Defuzzification. The dataset of tweets to classify and the result of the classification are stored in the Hadoop Distributed File System (HDFS), and we use the Hadoop MapReduce for the application of our proposal.

scholarly journals Sentiment Analysis of Patients' Opinions in Healthcare using Lexicon-based Method

2019 ◽  
Vol 9 (1) ◽  
pp. 6977-6981
Keyword(s):  
Social Networking ◽  
Sentiment Analysis ◽  
Social Networking Sites ◽  
Healthcare Service ◽  
Opinion Mining ◽  
Research Area ◽  
Service Needs ◽  
Health Organizations ◽  
The World ◽  
New Research

The rapid increase in technology made people across the world use social networking sites to express their opinions on a topic, product or service. The success of a healthcare service directly depends on its users. If a majority of users like the service then it is a success otherwise, the service needs to be improvised. For improvising the service, the users' opinions need to be analyzed. Manually extracting and analyzing the content present on the web is a tedious task. This gave rise to a new research area called Sentiment Analysis. It is otherwise known as opinion mining. It is being used by many health organizations to make effective decisions on their service. This paper presents the sentiment analysis of patients' opinions on hospitals which is mainly used to improve healthcare service. This is implemented using a lexicon-based methodology to analyze the sentiment.

scholarly journals Twitter Sentiment Analysis on Government Law Using Real Time Data

2021 ◽  
pp. 200-205
Author(s):  
Sujata Patil ◽  
Bhavesh Wagh ◽  
Aditya Bhinge ◽  
Aakash Sahal ◽  
Prof. Madhav Ingale
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Opinion Mining ◽  
Central Government ◽  
Research Work ◽  
Research Area ◽  
User Generated Content ◽  
Time Data ◽  
Social Data

Social media monitoring has been growing day by day so analyzing social data plays an important role in knowing people's behavior. So we are analyzing Social data such as Twitter Tweets using sentiment analysis which checks the opinion of people related to government schemes that are announced by the Central Government. This paper-based is on social media Twitter datasets of particular schemes and their polarity of sentiments. The popularity of the Internet has been rapidly increased. Sentiment analysis and opinion mining is the field of study that analyses people's opinions, sentiments, evaluations, attitudes, and emotions from written language. User-generated content is highly generated by users. The growing importance of sentiment analysis coincides with the growth of social media such as reviews, forum discussions, blogs, micro-blogs, Twitter, and social networks. It is difficult to analyze or summarize user-generated content. Most of the users write their opinions, thoughts on blogs, social media sites, E-commerce sites, etc. So these contents are very important for individuals, industry, government, and research work to make decisions. This Sentiment analysis and opinion mining research is a hot research area that comes under Natural Language processing. We plot and calculate numbers of positive, negative, and neutral tweets from each event.

ASA: A framework for Arabic sentiment analysis

2019 ◽  
Vol 46 (4) ◽  
pp. 544-559 ◽  
Author(s):  
Ahmed Oussous ◽  
Fatima-Zahra Benjelloun ◽  
Ayoub Ait Lahcen ◽  
Samir Belfkih
Keyword(s):  
Deep Learning ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Opinion Mining ◽  
Short Term Memory ◽  
Research Area ◽  
Support Vector ◽  
Learning Models ◽  
Arabic Natural Language Processing ◽  
Arabic Sentiment Analysis

Sentiment analysis (SA), also known as opinion mining, is a growing important research area. Generally, it helps to automatically determine if a text expresses a positive, negative or neutral sentiment. It enables to mine the huge increasing resources of shared opinions such as social networks, review sites and blogs. In fact, SA is used by many fields and for various languages such as English and Arabic. However, since Arabic is a highly inflectional and derivational language, it raises many challenges. In fact, SA of Arabic text should handle such complex morphology. To better handle these challenges, we decided to provide the research community and Arabic users with a new efficient framework for Arabic Sentiment Analysis (ASA). Our primary goal is to improve the performance of ASA by exploiting deep learning while varying the preprocessing techniques. For that, we implement and evaluate two deep learning models namely convolutional neural network (CNN) and long short-term memory (LSTM) models. The framework offers various preprocessing techniques for ASA (including stemming, normalisation, tokenization and stop words). As a result of this work, we first provide a new rich and publicly available Arabic corpus called Moroccan Sentiment Analysis Corpus (MSAC). Second, the proposed framework demonstrates improvement in ASA. In fact, the experimental results prove that deep learning models have a better performance for ASA than classical approaches (support vector machines, naive Bayes classifiers and maximum entropy). They also show the key role of morphological features in Arabic Natural Language Processing (NLP).

scholarly journals Sentiment Analysis using Bi-directional Recurrent Neural Network for Telugu Movies

2019 ◽  
Vol 9 (2) ◽  
pp. 241-245 ◽  
Keyword(s):  
Neural Network ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Recurrent Neural Network ◽  
Research Area ◽  
Support Vector ◽  
Common Space ◽  
The Common ◽  
Active Research ◽  
Regional Languages

Sentiment Analysis is the Natural Language Processing (NLP) is the active research area due to its vast application like stock market prediction, product re-views etc. The sentiment analysis in the regional languages are required for the film industries to increase their profit. Many existing methods has been applied on the sentiment analysis in the regional languages to increases the performance and still, it lags due in efficiency. In this research, the Bi-directional Recurrent Neural Network (BRNN) is applied to increase the performance of the sentiment analysis in the regional languages. The BRNN method has the advantages of rep-resenting the high and poor resources sentences in the common space and sentiment is analyzed based on the similarity measure. The proposed method is evaluated on the twitter data and compared this with the existing methods such as Random forest and Support Vector Machine (SVM). The proposed BRNN has the overall accuracy of 50.32%, while existing method of SVM has the overall accuracy of 38.73%.

scholarly journals Opinion Mining and Sentiment Analysis: A Survey

2012 ◽  
Vol 2 (3) ◽  
pp. 171-178 ◽  
Author(s):  
Mohammad Sadegh Hajmohammadi ◽  
Roliana Ibrahim ◽  
Zulaiha Ali Othman
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
Opinion Mining ◽  
Time Machine ◽  
Web Documents ◽  
Customer Reviews ◽  
The Past ◽  
New Research ◽  
Machine Readable ◽  
Research Domain

In the past few years, a great attention has been received by web documents as a new source of individual opinions and experience. This situation is producing increasing interest in methods for automatically extracting and analyzing individual opinion from web documents such as customer reviews, weblogs and comments on news. This increase was due to the easy accessibility of documents on the web, as well as the fact that all these were already machine-readable on gaining. At the same time, Machine Learning methods in Natural Language Processing (NLP) and Information Retrieval were considerably increased development of practical methods, making these widely available corpora. Recently, many researchers have focused on this area. They are trying to fetch opinion information and analyze it automatically with computers. This new research domain is usually called Opinion Mining and Sentiment Analysis. . Until now, researchers have developed several techniques to the solution of the problem. This paper try to cover some techniques and approaches that be used in this area.

scholarly journals VLSP SHARED TASK: SENTIMENT ANALYSIS

2019 ◽  
Vol 34 (4) ◽  
pp. 295-310 ◽  
Author(s):  
Huyen T M Nguyen ◽  
Hung V Nguyen ◽  
Quyen T Ngo ◽  
Luong X Vu ◽  
Vu Mai Tran ◽  
...  
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
Objective Evaluation ◽  
Shared Task ◽  
Electronic Products ◽  
Performance Quality ◽  
Benchmark Datasets ◽  
Active Research ◽  
Evaluation Measurement

Sentiment analysis is a natural language processing (NLP) task of identifying orextracting the sentiment content of a text unit. This task has become an active research topic since the early 2000s. During the two last editions of the VLSP workshop series, the shared task on Sentiment Analysis (SA) for Vietnamese has been organized in order to provide an objective evaluation measurement about the performance (quality) of sentiment analysis tools, and encouragethe development of Vietnamese sentiment analysis systems, as well as to provide benchmark datasets for this task. The rst campaign in 2016 only focused on the sentiment polarity classication, with a dataset containing reviews of electronic products. The second campaign in 2018 addressed the problem of Aspect Based Sentiment Analysis (ABSA) for Vietnamese, by providing two datasets containing reviews in restaurant and hotel domains. These data are accessible for research purpose via the VLSP website vlsp.org.vn/resources. This paper describes the built datasets as well as the evaluation results of the systems participating to these campaigns.

scholarly journals Which OCR toolset is good and why? A comparative study

2021 ◽  
Vol 48 (2) ◽  
Author(s):  
Pooja Jain ◽  
◽  
Dr. Kavita Taneja ◽  
Dr. Harmunish Taneja ◽  
◽  
...  
Keyword(s):  
Comparative Study ◽  
Language Processing ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Research Area ◽  
Real World Applications ◽  
Banking Education ◽  
Active Research ◽  
Active Research Area ◽  
Computational Technology

Optical Character Recognition (OCR) is a very active research area in many challenging fields like pattern recognition, natural language processing (NLP), computer vision, biomedical informatics, machine learning (ML), and artificial intelligence (AI). This computational technology extracts the text in an editable format (MS Word/Excel, text files, etc.) from PDF files, scanned or hand-written documents, images (photographs, advertisements, and alike), etc. for further processing and has been utilized in many real-world applications including banking, education, insurance, finance, healthcare and keyword-based search in documents, etc. Many OCR toolsets are available under various categories, including open-source, proprietary, and online services. This research paper provides a comparative study of various OCR toolsets considering a variety of parameters.

An Opinion Mining Approach for Drug Reviews in Spanish

2021 ◽  
pp. 445-480
Author(s):  
Karina Castro-Pérez ◽  
José Luis Sánchez-Cervantes ◽  
María del Pilar Salas-Zárate ◽  
Maritza Bustos-López ◽  
Lisbeth Rodríguez-Mazahua
Keyword(s):  
Language Processing ◽  
Opinion Mining ◽  
Adverse Drug Events ◽  
Hybrid Approach ◽  
Unstructured Data ◽  
Degenerative Diseases ◽  
Analysis System ◽  
Essential Contribution ◽  
Processing Techniques ◽  
Manual Review

In recent years, the application of opinion mining has increased as a boom and growth of social media and blogs on the web, and these sources generate a large volume of unstructured data; therefore, a manual review is not feasible. For this reason, it has become necessary to apply web scraping and opinion mining techniques, two primary processes that help to obtain and summarize the data. Opinion mining, among its various areas of application, stands out for its essential contribution in the context of healthcare, especially for pharmacovigilance, because it allows finding adverse drug events omitted by the pharmaceutical companies. This chapter proposes a hybrid approach that uses semantics and machine learning for an opinion mining-analysis system by applying natural-language-processing techniques for the detection of drug polarity for chronic-degenerative diseases, available in blogs and specialized websites in the Spanish language.

Multi-neural network-based sentiment analysis of food reviews based on character and word embeddings

2020 ◽  
pp. 002072092092849
Author(s):  
Yong Li ◽  
Qingyu Jin ◽  
Min Zuo ◽  
Haisheng Li ◽  
Xiaojun Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Chinese Character ◽  
Semantic Features ◽  
Word Embeddings ◽  
Emotional Information ◽  
Related Sequence ◽  
Active Research ◽  
Multi Neural Network

Sentiment analysis becomes one of the most active research hotspots in the field of natural language processing tasks in recent years. However, the inability to fully and effectively use emotional information is a problem in present deep learning models. A single Chinese character has different meanings in different words, and the character embeddings are combined with the word embeddings to extract more precise meaning information. In this paper, a single Chinese character and word are used as input units to train. Based on BLSTM, the attention mechanism based on vocabulary semantics in food field is introduced to realize distance-related sequence semantic feature extraction. CNN is used to realize semantic sentiment classification of sequence semantic features. Therefore, a model based on multi-neural network for sentiment information extraction and analysis is proposed. Experiments show that the model has excellent characteristics in sentiment analysis and obtains high accuracy and F value.

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.

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