Twitter Data Sentiment Analysis on a Malayalam Dataset Using Rule-Based Approach

2019 ◽  
pp. 407-415
Author(s):  
Deepa Mary Mathews ◽  
Sajimon Abraham
Keyword(s):  
Sentiment Analysis ◽  
Rule Based ◽  
Twitter Data ◽  
Rule Based Approach

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

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

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 Recognition and Effective Handling of Negations in Enhancing the Accuracy of Urdu Sentiment Analyzer

2020 ◽  
Vol 39 (4) ◽  
pp. 759-771
Author(s):  
Neelam Mukhtar ◽  
Mohammad Abid Khan ◽  
Nadia Chiragh ◽  
Asim Ullah Jan ◽  
Shah Nazir
Keyword(s):  
Sentiment Analysis ◽  
Negative Word ◽  
Long Distance ◽  
Rule Based ◽  
Sentiment Lexicon ◽  
Effective Role ◽  
Rule Based Approach ◽  
Implicit And Explicit ◽  
Multiple Domains

Although work has been done in Urdu Sentiment Analysis by researchers but still there is a lot of room for improvement in the form of achieving higher accuracy. Therefore, in this research, the accuracy of Urdu Sentiment Analysis in multiple domains is enhanced by dealing negations using Lexicon-based approach, one of the broadly used approaches for performing Sentiment Analysis. Negations in Urdu Sentiment Analysis are particularly focused in this research because of their effective role in Sentiment Analysis. Both local and long distance negations are considered. For achieving this goal, a corpus with 6025 Urdu sentences, from 151 blogs that belong to 14 different genres is taken in which use of negations is carefully observed. Two major steps are taken in this regard. First, to deal with the morphological negations, this type of negations is included in the negative word file of the Urdu Sentiment Lexicon developed for performing Sentiment Analysis of Urdu blogs. Secondly, rule-based approach is used for handling the implicit and explicit negations. Rules are designed that can deal with both implicit and explicit negations effectively. Implementation of these rules increased the accuracy of Sentiment Analyzer from 73.88% to 78.32% with 0.745, 0.788 and 0.745 Precision, Recall and Fmeasure respectively, which is statistically significant improvement.

scholarly journals Rule-based and Lightly Supervised Methods to Predict Emotions in Suicide Notes

2012 ◽  
Vol 5s1 ◽  
pp. BII.S8953 ◽  
Author(s):  
Ted Pedersen
Keyword(s):  
Sentiment Analysis ◽  
Corpus Analysis ◽  
Measures Of Association ◽  
Rule Based ◽  
The Third ◽  
Suicide Notes ◽  
Supervised Methods ◽  
Rule Based Approach ◽  
F Measure

This paper describes the Duluth systems that participated in the Sentiment Analysis track of the i2b2/VA/Cincinnati Children's 2011 Challenge. The top Duluth system was a rule-based approach derived through manual corpus analysis and the use of measures of association to identify significant ngrams. This performed in the median range of systems, attaining an F-measure of 0.45. The second system was automatically derived from the most frequent bigrams unique to one or two emotions. It achieved an F-measure of 0.36. The third system was the union of the first two, and reached an F-measure of 0.44.

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.

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