In this modern age, people are dependent on the internet. They prefer to order food online or Food App rather than the restaurant. They are giving various reviews online about the foods. In this project, we aim to build a machine learning model to analyze the sentiment of that reviews. In Bangladesh, internet users are increasing day by day. So we have decided to build the model for the Bangla language. We have found no Bangla dataset for food reviews that we can use for our project. Then we have collected more than one thousand Bangla food reviews from various online platforms like Foodpanda, Hungrynaki, Shohoz food, Pathao food, etc., and labeled them. After some necessary preprocessing, we have extracted various features from cleaned data and used them to train and test for machine learning and deep learning models. We have come to the result that Long Term Short Term (LSTM), a deep learning model giving the best accuracy, that is 90.89%, where we have used word2sequence as feature extraction. Our research contribution will help the food industry by using this model. This model can help them to understand the Bangla food review sentiment. 

Bangla Food Review Sentimental Analysis using Machine Learning

In this modern age, people are dependent on the internet. They prefer to order food online or Food App rather than the restaurant. They are giving various reviews online about the foods. In this project, we aim to build a machine learning model to analyze the sentiment of that reviews. In Bangladesh, internet users are increasing day by day. So we have decided to build the model for the Bangla language. We have found no Bangla dataset for food reviews that we can use for our project. Then we have collected more than one thousand Bangla food reviews from various online platforms like Foodpanda, Hungrynaki, Shohoz food, Pathao food, etc., and labeled them. After some necessary preprocessing, we have extracted various features from cleaned data and used them to train and test for machine learning and deep learning models. We have come to the result that Long Term Short Term (LSTM), a deep learning model giving the best accuracy, that is 90.89%, where we have used word2sequence as feature extraction. Our research contribution will help the food industry by using this model. This model can help them to understand the Bangla food review sentiment.

As a sort of technological advancement, social media is a medium used to transmit ideas on certain subjects. Sentiment analysis can be used to analyze public opinion. Feature extraction stage of sentiment analysis is crucial for transforming unstructured text into categorizable structured data. Using 13,297 records from Twitter and SVM algorithm, as well as the TF-IDF and Word2Vec feature extraction approaches, the combination of SVM + TF-IDF with 80:20 data split scenario and the RBF kernel produces the best results, with precision 85%, recall 86%, and f1-score 84%. In the 80:20 data split and RBF kernel, SVM+Word2Vec combination achieves the highest performance, with precision 83%, recall 82%, and f1-score 76%. 

The Implementation of TF-IDF and Word2Vec on Booster Vaccine Sentiment Analysis Using Support Vector Machine Algorithm

The world has recently seen unprecedented growth of social networks that have enabled internet users to share and communicate their views in various areas of life. Analysis of opinion is one type of text mining that aims to discover human views and emotions about any particular event, or topic. In the experiment of this research, we perform document level sentiment analysis in the form of positive and negative sentiments using both the traditional models and recent deep learning models. Three traditional methods (i.e. Naive Bayes (NB), Logistic Regression (LR), and Voting Ensemble) and three deep learning layout (i.e. Single Convolutional Neural Network (CNN), Single CNN + FastText and Ensemble(2-Layer CNN + Gated Recurrent Unit (GRU))) have been employed for two different datasets to evaluate the opinion classification. In our experiment, accuracy, f1-score, and RMSE have been considered as evaluation parameters and the optimistic results show that for airline review dataset, Single CNN + FastText comes out with the highest performance whereas for hotel review dataset, 2-L CNN + GRU comes out with the highest performance.

An Intense Investigation on Deep and Traditional Machine Learning Approaches for Better Sentiment Classification

This review addresses emerging literature in the field of Opinion Mining with a particular emphasis on user-generated textual content. The focus is on the various tasks involved in Opinion Mining, which satisfies the comprehension of the essential criteria and methodologies that should be considered prior to embarking on the task. The paper provides an in-depth analysis of benchmarked datasets, widely-used feature sets, algorithms, techniques, open-source tools, challenges, real-world applications, and insights into various dimensions of Opinion Mining. The findings have both theoretical and practical implications, as it highlights the significance of Opinion Mining of textual content in comprehensive society. This review covers both technical and real-world knowledge and offers a comprehensive understanding of available open-source tools that are used in real-time.

Opinion Mining Using Multi-Dimensional Analysis

Opinion analysis is one kind of text mining that aims to find out human opinions and emotions towards any particular event, topic, or subject. Feature extraction is one of the most crucial steps in the text categorization process and an efficient feature extraction method can greatly enhance the accuracy of opinion mining. In this paper, we try to concentrate on comparing the performances of four well-known feature extraction techniques (i.e. BOW, TF-IDF, Skip-Gram, and CBOW) for binary opinion classification. To accomplish the task, two different datasets of hotel and restaurant have been considered and three machine learning models i.e. Naive Bayes (NB), Support Vector Machine (SVM), and Logistic Regression (LR) have been applied as supervised classifiers. In terms of classifier accuracy and mean squared error (MSE), the experimented result depicts that Skip-Gram technique shows the highest performance among the four feature extraction techniques for both datasets. In addition, SVM and LG classifiers perform the best for hotel and restaurant datasets respectively and our proposed approach comes out with an optimistic accuracy of 95.8%.

A Comparative Analysis of Feature Extraction Methods for Human Opinion Grouping Using Several Machine Learning Techniques

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A Comparative Analysis of Feature Extraction Methods for Human Opinion Grouping Using Several Machine Learning Techniques