Sentiment Analysis of Text Classification Algorithms Using Confusion Matrix

2019 ◽  
pp. 231-241
Author(s):  
Babacar Gaye ◽  
Aziguli Wulamu
Keyword(s):  
Sentiment Analysis ◽  
Text Classification ◽  
Confusion Matrix ◽  
Classification Algorithms

Effectiveness of Domain-Based Lexicons vis-à-vis General Lexicon for Aspect-Level Sentiment Analysis: A Comparative Analysis

2019 ◽  
Vol 18 (03) ◽  
pp. 1950033
Author(s):  
Madan Lal Yadav ◽  
Basav Roychoudhury
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Text Classification ◽  
Machine Learning Techniques ◽  
Classification Algorithms ◽  
Actual Performance ◽  
Learning Techniques ◽  
Improved Performance ◽  
Text Content ◽  
Better Than

One can either use machine learning techniques or lexicons to undertake sentiment analysis. Machine learning techniques include text classification algorithms like SVM, naive Bayes, decision tree or logistic regression, whereas lexicon-based sentiment analysis uses either general or domain-based lexicons. In this paper, we investigate the effectiveness of domain lexicons vis-à-vis general lexicon, wherein we have performed aspect-level sentiment analysis on data from three different domains, viz. car, guitar and book. While it is intuitive that domain lexicons will always perform better than general lexicons, the actual performance however may depend on the richness of the concerned domain lexicon as well as the text analysed. We used the general lexicon SentiWordNet and the corresponding domain lexicons in the aforesaid domains to compare their relative performances. The results indicate that domain lexicon used along with general lexicon performs better as compared to general lexicon or domain lexicon, when used alone. They also suggest that the performance of domain lexicons depends on the text content; and also on whether the language involves technical or non-technical words in the concerned domain. This paper makes a case for development of domain lexicons across various domains for improved performance, while gathering that they might not always perform better. It further highlights that the importance of general lexicons cannot be underestimated — the best results for aspect-level sentiment analysis are obtained, as per this paper, when both the domain and general lexicons are used side by side.

scholarly journals Election Result Prediction Using Sentiment Analysis

2021 ◽  
pp. 118-122
Author(s):  
Ankit Maurya ◽  
Satish Lodh ◽  
Mayur Joshi ◽  
Prof. Vinaykumar Singh
Keyword(s):  
Sentiment Analysis ◽  
Text Classification ◽  
Political Party ◽  
Daily Basis ◽  
Unlabeled Data ◽  
Classification Algorithms ◽  
Communication Tool ◽  
Specific Topic ◽  
Election Result ◽  
Pie Chart

Social media today has become a very popular communication tool for users. Millions of users share their opinions on different aspects on daily basis. Sentiment Analysis determines the polarity and inclination towards any specific topic, idea or entity. Applications of such analysis can be seen during elections, movie promotions, and many other fields. In our project, we aim to predict the winning probability of any political party by using both labelled as well as unlabeled data. Labelled data can be collected by using polling method but the result may not provide better accuracy. Hence, it is necessary to fetch live data to predict the accurate election result. Twitter is a microblogging site which allows the users in posting quick and real-time updates about different activities or events as the spread of information and news is quick enough. With the help of hashtags, the needed data can be easily generated and put to use. We exploited the python library “Tweepy” for accessing the Twitter API and fetched live data from Twitter. 350 tweets for each political party are fetched by using keywords. Using “TextBlob” library of python, sentiments are applied to each tweet and depending upon more positive tweets for particular party, winning party is declared. Also, popular text classification algorithms like Na¨ıve Bayes, SVM and Random Forest are used to train model using labelled data. The accuracy of the predicted result is calculated and the result is declared Finally, result is represented in the form of bargraph for labelled data according to the number of voted for each political party and for unlabeled data using pie chart for each political party representing positive, negative and neutral sentiments.

Deep Learning for text in limted data settings

2020 ◽  
Author(s):  
Pathikkumar Patel ◽  
Bhargav Lad ◽  
Jinan Fiaidhi
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Transfer Learning ◽  
Text Classification ◽  
State Of The Art ◽  
Time Series Forecasting ◽  
Text Data ◽  
Performance Levels

During the last few years, RNN models have been extensively used and they have proven to be better for sequence and text data. RNNs have achieved state-of-the-art performance levels in several applications such as text classification, sequence to sequence modelling and time series forecasting. In this article we will review different Machine Learning and Deep Learning based approaches for text data and look at the results obtained from these methods. This work also explores the use of transfer learning in NLP and how it affects the performance of models on a specific application of sentiment analysis.

Headnote Prediction Using Machine Learning

2021 ◽  
Vol 18 (5) ◽  
Author(s):  
Sarmad Mahar ◽  
Sahar Zafar ◽  
Kamran Nishat
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Active Learning ◽  
Text Classification ◽  
Extraction Methods ◽  
Text Summarization ◽  
Training Data ◽  
Second Step ◽  
Support Vector ◽  
Classification Algorithms

Headnotes are the precise explanation and summary of legal points in an issued judgment. Law journals hire experienced lawyers to write these headnotes. These headnotes help the reader quickly determine the issue discussed in the case. Headnotes comprise two parts. The first part comprises the topic discussed in the judgment, and the second part contains a summary of that judgment. In this thesis, we design, develop and evaluate headnote prediction using machine learning, without involving human involvement. We divided this task into a two steps process. In the first step, we predict law points used in the judgment by using text classification algorithms. The second step generates a summary of the judgment using text summarization techniques. To achieve this task, we created a Databank by extracting data from different law sources in Pakistan. We labelled training data generated based on Pakistan law websites. We tested different feature extraction methods on judiciary data to improve our system. Using these feature extraction methods, we developed a dictionary of terminology for ease of reference and utility. Our approach achieves 65% accuracy by using Linear Support Vector Classification with tri-gram and without stemmer. Using active learning our system can continuously improve the accuracy with the increased labelled examples provided by the users of the system.

scholarly journals Analysis of Text Classification Algorithms: A Review

2019 ◽  
Vol Volume-3 (Issue-2) ◽  
pp. 579-581
Author(s):  
Nida Zafar Khan ◽  
Prof. S. R. Yadav ◽  
Keyword(s):  
Text Classification ◽  
Classification Algorithms

scholarly journals SENTIMENT ANALYSIS OF ELECTRIC CARS USING RECURRENT NEURAL NETWORK METHOD IN INDONESIAN TWEETS

Kursor ◽  
2020 ◽  
Vol 10 (4) ◽  
Author(s):  
Felisia Handayani ◽  
Metty Mustikasari
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Recurrent Neural Network ◽  
Confusion Matrix ◽  
Data Representation ◽  
Sequential Data ◽  
Communication Tool ◽  
Electric Cars

Sentiment analysis is computational research of the opinions of many people who are textually expressed against a particular topic. Twitter is the most popular communication tool among Internet users today to express their opinions. Deep Learning is a solution to allow computers to learn from experience and understand the world in terms of the hierarchy concept. Deep Learning objectives replace manual assignments with learning. The development of deep learning has a set of algorithms that focus on learning data representation. The recurrent Neural Network is one of the machine learning methods included in Deep learning because the data is processed through multi-players. RNN is also an algorithm that can recall the input with internal memory, therefore it is suitable for machine learning problems involving sequential data. The study aims to test models that have been created from tweets that are positive, negative, and neutral sentiment to determine the accuracy of the models. The models have been created using the Recurrent Neural Network when applied to tweet classifications to mark the individual classes of Indonesian-language tweet data sentiment. From the experiments conducted, results on the built system showed that the best test results in the tweet data with the RNN method using Confusion Matrix are with Precision 0.618, Recall 0.507 and Accuracy 0.722 on the data amounted to 3000 data and comparative data training and data testing of ratio data 80:20

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