A rule based approach for aspect extraction in hindi reviews

2021 ◽  
pp. 1-9
Author(s):  
Chinmayee Ojha ◽  
Manju Venugopalan ◽  
Deepa Gupta
Keyword(s):  
Public Opinion ◽  
Social Networking ◽  
Sentiment Analysis ◽  
Fast Growth ◽  
Rule Based ◽  
The Public ◽  
Aspect Extraction ◽  
Automated Process ◽  
Rule Based Approach ◽  
Baseline Approach

Fast growth of technology and the tremendous growth of population has made millions of people to be active participants on social networking forums. The experiences shared by the participants on different websites is highly useful not only to customers to make decisions but also helps companies to maintain sustainability in businesses. Sentiment analysis is an automated process to analyze the public opinion behind certain topics. Identifying targets of user’s opinion from text is referred to as aspect extraction task, which is the most crucial and important part of Sentiment Analysis. The proposed system is a rule-based approach to extract aspect terms from reviews. A sequence of patterns is created based on the dependency relations between target and its nearby words. The system of rules works on a benchmark of dataset for Hindi shared by Akhtar et al., 2016. The evaluated results show that the proposed approach has significant improvement in extracting aspects over the baseline approach reported on the same dataset.

Analysis of Public Sentiments About Mega Online Sale Using Tweets on Big Billions Day Sale

2019 ◽  
pp. 59-76 ◽  
Author(s):  
Dilip Singh Sisodia ◽  
Ritvika Reddy
Keyword(s):  
Social Networking ◽  
Sentiment Analysis ◽  
Stock Exchange ◽  
The Public ◽  
Online Sales ◽  
Public Sentiment ◽  
Online Platforms ◽  
Wide Range ◽  
Automated Process ◽  
The Web

The opinion of others significantly influences our decision-making process about any product or service. The positive or negative opinions of prospective clients or customers may promote or demote the profit margin of any business activities. Therefore, analyzing the public sentiment is important for many applications such as firms trying to find out the response of their products in the market, predicting political elections, and predicting socioeconomic phenomena such as stock exchange, sale of products, etc. With the emergence of Web 2.0 services, a wide range of online platforms including micro-blogging, social networking, and many other review platforms are available. The automated process for public sentiment analysis from a large amount of social data present on the web helps to improve customer satisfaction. This chapter discusses the process of sentiment analysis of prospective buyers of mega online sales using their posted tweets about the big billions day sale.

scholarly journals A Mixed approach of Deep Learning method and Rule-Based method to improve Aspect Level Sentiment Analysis

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Paramita Ray ◽  
Amlan Chakrabarti
Keyword(s):  
Deep Learning ◽  
Sentiment Analysis ◽  
State Of The Art ◽  
The State ◽  
Learning Approach ◽  
Learning Method ◽  
Rule Based ◽  
Aspect Extraction ◽  
Art Methods ◽  
Rule Based Approach

Social networks have changed the communication patterns significantly. Information available from different social networking sites can be well utilized for the analysis of users opinion. Hence, the organizations would benefit through the development of a platform, which can analyze public sentiments in the social media about their products and services to provide a value addition in their business process. Over the last few years, deep learning is very popular in the areas of image classification, speech recognition, etc. However, research on the use of deep learning method in sentiment analysis is limited. It has been observed that in some cases the existing machine learning methods for sentiment analysis fail to extract some implicit aspects and might not be very useful. Therefore, we propose a deep learning approach for aspect extraction from text and analysis of users sentiment corresponding to the aspect. A seven layer deep convolutional neural network (CNN) is used to tag each aspect in the opinionated sentences. We have combined deep learning approach with a set of rule-based approach to improve the performance of aspect extraction method as well as sentiment scoring method. We have also tried to improve the existing rule-based approach of aspect extraction by aspect categorization with a predefined set of aspect categories using clustering method and compared our proposed method with some of the state-of-the-art methods. It has been observed that the overall accuracy of our proposed method is 0.87 while that of the other state-of-the-art methods like modified rule-based method and CNN are 0.75 and 0.80 respectively. The overall accuracy of our proposed method shows an increment of 7–12% from that of the state-of-the-art methods.

scholarly journals Rule-Based Approach for Party-Based Sentiment Analysis in Legal Opinion Texts

2020 ◽  
Author(s):  
Isanka Rajapaksha ◽  
Chanika Ruchini Mudalige ◽  
Dilini Karunarathna ◽  
Nisansa de Silva ◽  
Gathika Rathnayaka ◽  
...  
Keyword(s):  
Sentiment Analysis ◽  
Rule Based ◽  
Rule Based Approach ◽  
Legal Opinion

Aspect Based Sentiment Analysis of Unlabeled Reviews using Linguistic Rule Based LDA

2022 ◽  
Vol 24 (3) ◽  
pp. 0-0
Keyword(s):  
Social Networks ◽  
Sentiment Analysis ◽  
Domain Knowledge ◽  
Latent Dirichlet Allocation ◽  
Rule Based ◽  
Aspect Extraction ◽  
Linguistic Rule ◽  
Digital Era ◽  
Average Accuracy ◽  
Linguistic Rules

In this digital era, people are very keen to share their feedback about any product, services, or current issues on social networks and other platforms. A fine analysis of these feedbacks can give a clear picture of what people think about a particular topic. This work proposed an almost unsupervised Aspect Based Sentiment Analysis approach for textual reviews. Latent Dirichlet Allocation, along with linguistic rules, is used for aspect extraction. Aspects are ranked based on their probability distribution values and then clustered into predefined categories using frequent terms with domain knowledge. SentiWordNet lexicon uses for sentiment scoring and classification. The experiment with two popular datasets shows the superiority of our strategy as compared to existing methods. It shows the 85% average accuracy when tested on manually labeled data.

scholarly journals Opinion Mining and Sentiment Analysis on Big Data

2020 ◽  
pp. 410-415
Author(s):  
Shruti Rajkumar Choudhary
Keyword(s):  
Artificial Intelligence ◽  
Social Networking ◽  
Sentiment Analysis ◽  
Text Analysis ◽  
Opinion Mining ◽  
Stock Exchange ◽  
Daily Basis ◽  
Text Data ◽  
The Public ◽  
Public Sentiment

<p>Opinion mining is extract subjective information from text data using tools such as NLP, text analysis etc. Automated opinion mining often uses machine learning, a type of artificial intelligence (AI), to mine text for sentiment. Opinion mining, which is also called sentiment analysis, involves building a system to collect and categorize opinions about a product.In this project the problem of sentiment analysis in twitter; that is classifying tweets according to the sentiment expressed in terms of positive, negative or neutral. Twitter is an online micro-blogging and social-networking platform which allows users to write short status updates of maximum length 140 characters. It is a rapidly expanding service with over 200 million registered users out of which 100 million are active users and half of them log on twitter on a daily basis - generating nearly 250 million tweets per day. Due to this large amount of usage we hope to achieve a reflection of public sentiment by analysing the sentiments expressed in the tweets. Analysing the public sentiment is important for many applications such as firms trying to find out the response of their products in the market, predicting political elections and predicting socioeconomic phenomena like stock exchange.</p>

14. Protecting the public interest

Contract Law ◽  
2017 ◽  
Author(s):  
TT Arvind
Keyword(s):  
Public Interest ◽  
Supreme Court Decision ◽  
Contract Law ◽  
Rule Based ◽  
The Public ◽  
The Supreme Court ◽  
Good Government ◽  
Illegal Acts ◽  
Rule Based Approach ◽  
Restraint Of Trade

This chapter examines how English law deals with contracts against the public interest under the doctrine of illegality. The doctrine of illegality reflects a broader principle that applies across private law, that legal actions cannot be founded on illegal acts. In contract law, its implication is that contracts contrary to law or public policy are void. The chapter first considers the problem of illegal behaviour in contracting before discussing the rule-based approach to illegality and its limits. It then reviews the Supreme Court decision in Patel v Mirza and how it gave rise to the ‘range of factors’ approach to illegality. It also looks at criteria that make a contract illegal, including cases where the illegality consisted of criminal and civil wrongs. The chapter concludes with an overview of other types of illegality, such as the ‘injurious to good government’ ground and restraint of trade.

14. Protecting the public interest

Contract Law ◽  
2019 ◽  
pp. 404-426
Author(s):  
TT Arvind
Keyword(s):  
Public Interest ◽  
Supreme Court Decision ◽  
Contract Law ◽  
Rule Based ◽  
The Public ◽  
The Supreme Court ◽  
Good Government ◽  
Illegal Acts ◽  
Rule Based Approach ◽  
Restraint Of Trade

This chapter examines how English law deals with contracts against the public interest under the doctrine of illegality. The doctrine of illegality reflects a broader principle that applies across private law, that legal actions cannot be founded on illegal acts. In contract law, its implication is that contracts contrary to law or public policy are void. The chapter first considers the problem of illegal behaviour in contracting before discussing the rule-based approach to illegality and its limits. It then reviews the Supreme Court decision in Patel v Mirza and how it gave rise to the ‘range of factors’ approach to illegality. It also looks at criteria that make a contract illegal, including cases where the illegality consisted of criminal and civil wrongs. The chapter concludes with an overview of other types of illegality, such as the ‘injurious to good government’ ground and restraint of trade.

Crowdsourcing Investigations

2014 ◽  
Vol 6 (4) ◽  
pp. 60-75 ◽  
Author(s):  
Andrea H. Tapia ◽  
Nicolas J. LaLone
Keyword(s):  
Public Opinion ◽  
Sentiment Analysis ◽  
Ethical Dilemmas ◽  
The Internet ◽  
Social Approval ◽  
Online Groups ◽  
The Public ◽  
Large Numbers ◽  
Boston Marathon Bombing ◽  
Boston Marathon

In this paper the authors illustrate the ethical dilemmas that arise when large public investigations in a crisis are crowdsourced. The authors focus the variations in public opinion concerning the actions of two online groups during the immediate aftermath of the Boston Marathon Bombing. These groups collected and organized relief for victims, collected photos and videos taken of the bombing scene and created online mechanisms for the sharing and analysis of images collected online. They also used their large numbers and the affordances of the Internet to produce an answer to the question, “who was the perpetrator, and what kind of bomb was used?” The authors view their actions through public opinion, through sampling Twitter and applying a sentiment analysis to this data. They use this tool to pinpoint moments during the crisis investigation when the public became either more positively or negatively inclined toward the actions of the online publics. The authors use this as a surrogate, or proxy, for social approval or disapproval of their actions, which exposes large swings in public emotion as ethical lines are crossed by online publics.

Sentiment analysis from travellers’ reviews using enhanced conjunction rule based approach for feature-specific evaluation of hotels

2020 ◽  
Vol 23 (6) ◽  
pp. 983-997
Author(s):  
Aranyak Maity ◽  
Sritama Ghosh ◽  
Saikat Karfa ◽  
Moutan Mukhopadhyay ◽  
Saurabh Pal ◽  
...  
Keyword(s):  
Sentiment Analysis ◽  
Rule Based ◽  
Rule Based Approach ◽  
Specific Evaluation
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