Data Mining Practical Machine Learning Tools and Techniques

Recently, determining the customer impression is considered one of the prominent factors on the success of the restaurant businesses. Due to the rapid growth of digital contents related to restaurant or foods in the web, people are more inclined on reviews before going to any restaurant so the significance of customer review is inevitable. In order to selects a restaurant customer needs to check thousands of feedback’s to understand the restaurant quality or services. Therefore, classification of a significant amount of reviews into a sentimental category is required to attain meaningful insights so that the customer can choose restaurants based on their preferences. This classification can be done by sentiment analysis. This paper proposes a system that can classify customer reviews into positive and negative classes based on their sentimental feedback. We have tested the proposed system with 1000 restaurant reviews text written in Bengali. The experimental result shows that the proposed the system can classify restaurant reviews with 80.48% accuracy using multinomial Naive Bayes.

Sentiment Analysis of Bengali Texts on Online Restaurant Reviews Using Multinomial Naïve Bayes

Recently, sentiment polarity detection has increased attention to NLP researchers due to the massive availability of customer’s opinions or reviews in the online platform. Due to the continued expansion of e-commerce sites, the rate of purchase of various products, including books, is growing enormously among the people. Reader’s opinions/reviews affect the buying decision of a customer in most cases. This work introduces a machine learning-based technique to determine sentiment polarities (either positive or negative category) from Bengali book reviews. To assess the effectiveness of the proposed technique, a corpus with 2000 reviews on Bengali books is developed. A comparative analysis with various approaches (such as logistic regression, naive Bayes, SVM, and SGD) also performed by taking into consideration of the unigram, bigram, and trigram features, respectively. Experimental result reveals that the multinomial naive Bayes with unigram feature outperforms the other techniques with \(84\%\) accuracy on the test set.

Sentiment Polarity Detection on Bengali Book Reviews Using Multinomial Naïve Bayes

Nowadays, people used to express their feelings, thoughts, suggestions and opinions on different social platform and video sharing media. Many discussions are made on Twitter, Facebook and many respective forums on sports especially cricket and football. The opinion may express criticism in different manner, notation that may comprise different polarity like positive, negative or neutral and it is a challenging task even for human to understand the sentiment of each opinion as well as time consuming. This problem can be solved by analyzing sentiment in respective comments through natural language processing (NLP). Along with the success of many deep learning domains, Recurrent Neural Network (RNN) with Long-Short-Term-Memory (LSTM) is popularly used in NLP task like sentiment analysis. We have prepared a dataset about cricket comment in Bangla text of real people sentiments in three categories i.e. positive, negative and neutral and processed it by removing unnecessary words from the dataset. Then we have used word embedding method for vectorization of each word and for long term dependencies we used LSTM. The accuracy of this approach has given 95% that beyond the accuracy of previous all method.

Cricket Sentiment Analysis from Bangla Text Using Recurrent Neural Network with Long Short Term Memory Model

Sentiment is meant by feelings-attitudes, emotions and opinions. Sentiment polarity detection is one of most popular sentiment analysis tasks. Now-a-days, celebrities as well as common people write a huge amount of blog posts, tweets and comments on the social media. Such social media texts are also written in Indian languages. The research on sentiment analysis in Indian language domain is also at the early stage. In this paper, we present a sentiment polarity detection approach that detects sentiment polarity of Bengali tweets using character ngram features and a supervised machine learning algorithm called Multinomial Na\"{\ive Bayes. Our proposed approach has been tested on the SAIL 2015 dataset. The experimental results show that character n-gram features are more effective than the traditional word n-gram features. The overall performance of our proposed system is also significantly better than some existing sentiment polarity detection systems.

Using Character N-gram Features and Multinomial Naïve Bayes for Sentiment Polarity Detection in Bengali Tweets

Sentiment analysis is an area of study that deals with extraction, identification or otherwise characterization of the sentiment content of written text units Sentiment is meant by feelings-attitudes, emotions and opinions Sentiment polarity detection is one of most popular sentiment analysis tasks Now-a-days, blog posts, tweets and comments in Indian languages are available on the web in a large number Sentiment analysis in Indian languages is relatively new area and research on sentiment analysis in Indian language domain is at the early stage In this paper, we present a sentiment polarity detection approach that detects sentiment polarity of Bengali tweets using machine learning algorithms Our proposed approach has been tested on the Bengali tweet dataset released for SAIL contest 2015 The experimental results show that performance of our proposed system is better than the best system participated in SAIL 2015 sentiment analysis contest

Mandira Bhowmick

Papers

Sentiment polarity detection in bengali tweets using multinomial Naïve Bayes and support vector machines