Multilingual Opinion Mining Movie Recommendation System Using RNN

Everyone in this rapid evolution generation makes use of online resources. There is a huge amount of textual content available online from several sources, including social media apps, e-commerce sites, movie recommendation system, etc. Everyone in the modern society provides a variety of reviews for a specific product or item. An effective and smart technique is required that can analyze and provide the polarity of this textual data. since it is impossible to manually review such a vast volume of user-generated data. For this user-generated data, automatic sentiment classification is possible using a variety of tools and methods. Opinion mining and sentiment classification benefit from the dependable and accuracy of machine learning. Moreover, the sentiment analysis and evaluation process are fraught with difficulties. These complexities make it difficult to interpret sentiments accurately and figure out the right sentiment polarity. This article describes an overview on sentiment analysis(SA) applications, challenges, and issues encountered by researchers, with the goal of providing a systematic review on sentiment analysis and related areas for future research.

Objective: To make an extensive analysis of sentiment within the discourse surrounding COVID-19 vaccines on Twitter, employing Natural Language Processing (NLP) methodologies. Method: The research methodology encompasses data collection via the Twitter API, followed by sentiment analysis facilitated by the TextBlob library. Pre-processing stages are integrated to cleanse and standardize the Twitter data. Subsequently, sentiment analysis categorizes tweets into positive, negative, and neutral sentiments based on polarity scores. Findings: The findings, grounded in a dataset spanning from March 1, 2022, to April 30, 2022, comprising 61,934 tweets, unveil that 45.0% of tweets conveyed positive sentiment, 17.3% exhibited negativity, and 37.7% maintained neutrality. Moreover, an exploration of tweet subjectivity revealed that 70.1% of the content expressed subjectivity, while 29.9% conveyed objectivity. The research is augmented with visual representations, including word clouds and subjectivity-polarity graphs, that offer a more intuitive understanding of sentiment trends. Novelty: This study contributes to the expanding landscape of sentiment analysis and its application within the context of public health crises, empowering stakeholders with valuable knowledge to enhance vaccine acceptance and effectiveness. The tool used “Tweet Downloader” in data collection makes this study different from other reviewed studies. Keywords: COVID-19 vaccine, Twitter sentiment analysis, Public perception, Natural Language Processing (NLP), Social media data

Movie recommendation systems have become ubiquitous in most sides of our lives. Currently, they are far from optimal. This paper presents a movielense recommendation system based on machine learning through utilizing the deep convolutional network and depending on generative modeling of public previous aspects mixtures. The objective of this paper is to introduce such a recommendation system to help users in selecting datasets of movies according to certain pre-specified measurements and data. The applied methodology is pivoted on implementing the system by using different sentimental analysis algorithms. These algorithms are keen to provide a solution for the full stack developers through using a trained model using their datasets. This will give suggestions based on their previous activity or recommended by other users’ interests demonstrated on their website. Thus to help users visualize their interest or to form the better scope of visualization. The presented system has proved better results concerning accuracy and efficiency in comparison with some other similar works. When experimentations on both real and synthetic datasets were conducted, the system showed percentile improvement of about 91.07%in the training dataset and 93.49%in the testing dataset respectively. This system is convenient for several application fields like time series network visualization, business process modeling, various data mining applications, e-commerce websites, besides most online platforms that people use including social media.

In order to analyze opinion mining from e-commerce websites, this paper discusses sentence-level sentiment analysis. To save time, many consumers use e-commerce websites like Amazon and eBay to purchase millions of things online. The product may already have a lot of reviews or comments from other customers who have already purchased it. In this paper, a Bayesian-network based framework is presented that makes use of product reviews imported from e-commerce websites for a specific product. The shipped product reviews are then automatically analyzed to produce untrained rules. After that, a Bayesian Network is constructed utilizing a set of untrained rules, evidence set and random variables. The newly generated reviews from e-commerce websites can subsequently be evaluated by the trained network. Additionally, the built-in model can be incrementally retrained to increase its resilience. Last but not the least, the framework is easily scalable to opinion mining from social networking sites on a variety of topics, and the model standards that are produced by this process are developed instantly and automatically.

Twitter, a micro-blogging site, is a huge repository of public opinions expressed towards various people, services, organizations, products etc. Sentiment analysis is the process of analyzing those public opinions. Sentiment analysis when combined with twitter gives useful insights into what is expressed on Twitter. It has applications in a number of domains like in stock market prediction, election results prediction, movie revenues, product reviews etc. This paper discusses about the different machine learning and deep learning models trained on a dataset of tweets collected from online github directory. The proposed system has been validated using a case study of election results prediction of February 2017 Punjab elections by analyzing public sentiments as expressed on twitter towards various political leaders. The developed SA system performs real-time sentiment analysis presenting analysis results simultaneously as tweets occur on twitter. A dashboard has been designed for presenting results to the user. The display updates every one minute, bringing tweets in real time and presenting the results graphically. Tweets are also getting stored in a CSV file for final results prediction. The system gave very encouraging results with minimal diversion when compared with actual results.

Objectives: This study aims to prepare a corpus and explore sentiment analysis in the Amharic language, which is increasingly used due to the growth of both the language and the Internet. Methods: The study acquired 23,646 Amharic tweets from Twitter using the Twitter API, cleaned and normalized the text through preprocessing, and manually annotated the data as positive, negative, or neutral by three annotators. The study utilized a multi-scale sentiment analysis approach to experimentally evaluate the classifier\'s performance and compare different ML and DL classifiers. Findings: The study found that sentiment analysis in the Amharic language in this dataset showed that the KNN classifier could classify texts with an accuracy of 76% and 90% accuracy using the CNN deep learning classifier. Novelty: This study contributes to the field of sentiment analysis by addressing the scarcity of an Amharic-language dataset specifically tailored for sentiment analysis purposes. Our approach involves filling this critical research gap by developing a new dataset. Subsequently, we employ machine learning and deep learning classifiers to assess the viability of this dataset for performing multi-class sentiment analysis tasks in the Amharic language. Keywords: Amharic, Sentiment Analysis, Multi­class, Machine Learning, Deep Learning Classifier

<p>In March of 2020, the World Health Organization identified COVID-19 as a new pandemic and issued a statement to that effect. This fatal virus was able to disperse and propagate throughout several countries all over the world. During the progression of the pandemic, social networking sites like Twitter generated significant and substantial volumes of data that helped improve the quality of decisions pertaining to health care applications. In this paper, we proposed a sentiment classification using various feature extraction and machine leavening techniques for social media dataset. The system has divided into four phase data collection, preprocessing and normalization, feature extraction and feature selection and finally classification. In first phase we collect data from social media sources such as twitter using Twitter API. In second phase the tweets, data was ready for preprocessing and it was sorted into three categories: positive, neutral, and negative. During the third phase, various features were extracted from the tweets by employing a number of widely utilized approaches, including as bag of words (BoW), Term Frequency-Inverse Document Frequency (TF-IDF), Word2Vec, and FastText, to gather feature datasets. These methods were employed to extract distinct datasets for the features. The final phase different machine learning classification algorithms are applied for detection of sentiment using machine learning. In the extensive experimental analysis, the BoW performed better results with modified support vector machine (mSVM) than existing machine learning algorithms. The proposed mSVM performed superiorly to the other classifiers by 98.15% accuracy rate. Once the tweets are correctly classified as COVID-19 tweets, it is further categorized into three sentiments that is positive, negative and neural. Proposed mSVM achieves 93% of accuracy rate for positive sentiment which better as compared to other Machine Learning (ML) classifiers.</p>

An effective collaborative movie recommender system with cuckoo search

The rapid growth of internet facilities has increased the comments, posts, blogs, feedback, etc., on a large scale on social networking sites. These social media data are available in an unstructured form, which includes images, text, and videos. The processing of these data is difficult, but some sentiment analysis, information retrieval, and recommender systems are used to process these unstructured data. To extract the opinion and sentiment of internet users from their written social media text, a sentiment analysis system is required to develop, which can work on both monolingual and bilingual phonetic text. Therefore, a sentiment analysis (SA) system is developed, which performs well on different domain datasets. The system performance is tested on four different datasets and achieved better accuracy of 3% on social media datasets, 1.5% on movie reviews, 1.35% on Amazon product reviews, and 4.56% on large Amazon product reviews than the state-of-art techniques. Also, the stemmer (StemVerb) for verbs of the English language is proposed, which improves the SA system's performance.

In today's very busy world, recommendation systems are becoming increasingly crucial. In a 24-hour period, people are always pressed for time because of the many activities they must do. Deshalb, the Movie recommendation systems are vital, as they help cinephiles find the finest movies to watch without having to waste their cognitive resources which are limited. This project aims to propose a movie recommendation system using the concept of knowledge graph and particle filtering. While every existing movie recommendation system depends on machine learning and clustering algorithms. This research work proposes a novel movie recommender system that operates directly on the database and is extremely efficient by utilizing the expressive potential of Knowledge Graphs and provides the user with the best recommendations. Using particle filtering and a knowledge graph database, this movie recommendation system provides the user with the best suitable movies depending on numerous parameters such as the user's favourite Directors, genre, etc. Unlike other systems, this recommendation system does not keep track of the browser history for future recommendations.

Social media has been a real-world sensor to observe the pulse of society. Although it provides unique communication opportunities, it also brings along vital challenges. One of them is hate speech, which attacks a single individual or targeted groups. Previous researchers claim that among social networks, the Twitter platform is mostly used to spread hate speech. However, data on a larger scale makes it hard to capture and understand the nature of hate speech on Twitter within specific food brands. In this study, sentiment analysis techniques were used to filter hate speech on Twitter and on three popular food brands in Malaysia. This study was conducted in five phases, namely raw data collection, pre-processing, sentiment analysis, visualization, and performance evaluation. This corpus consists of 28,898 data samples based on user tweet searches. A Twitter API was created and SQLite was used to store all the sample data. VADER sentiment analysis is used to classify tweet sentiment into positive, negative, and neutral. In the visualization phase, these three food brands are visualized using a histogram to gain sentiment analysis insights. Then, three machine learning methods were implemented to predict the best model for sentiment analysis. The Decision Tree classifier outperforms the average accuracy in Support Vector Machine and Logistic Regression with 99.99% for the training data set. This study provides insights to assist humans in making decisions. With the growth of opinions expressed in multimedia on social media, such as spoken feedback on Twitter, sentiment analysis has the potential to become a more news aggregation and low-cost endeavour.

Sentiment analysis refers to the automated process of identifying the underlying emotional tone conveyed in a text. This task has become increasingly crucial today due to the exponential growth of opinion-oriented text generation on various online platforms including social media and e-commerce websites. The process of sentiment analysis has evolved through different phases like the use of handcrafted rules, sentiment-oriented dictionaries, machine learning, deep learning, transformer models, and large language models. These different phases have enriched the field of sentiment analysis and enabled it to get better insights into text for sentiment analysis. One of the most impactful developments in sentiment analysis was the creation of embedding’s for words or sentences, which are useful for representing unstructured data as structured data. These embedding are representing words or sentences as numerical vectors in a high-dimensional space and have proven effective in capturing the semantic relationships between words. By leveraging the power of embedding’s, sentiment analysis models can understand the complexities of human language and provide more precise insights into people's emotions and opinions. However, word embedding’s not inherently generated for sentiment analysis, which means that they are not inherently sentiment-oriented. Therefore, many researchers have focused on developing sentiment-oriented word embedding’s for sentiment words, but they have overlooked the importance of intensity words such as “little”, “high”, and “extremely”. These intensity words determine the level of sentiment expressed in text, which is particularly useful for fine-level sentiment analysis. To address this issue, a review proposes an intensity-aware word embedding development model and sentiment analysis with ML.

Hybrid Movie Recommendation System using sentiment analysis with opinion mining, is a system that helps the user by giving recommendations about the movies, based on the user preference and ordinal ratings of the movies. It removes the need for the user to go through a big list of movies before deciding what should be watched. The arrangement exploits the consumer attitude and their ideas around the attributes to give recommendations and produce output. The script centred assessments from the consumer are very optimum in enlightening the accurateness and competence of a movie recommendation arrangement. Three methods must be castoff to abstract attributes as of the movies characteristics to be utilized as attributes for collaborative filtering. The essential physical interfaces are dissimilar for all the methods. The dataset that was cast-off for the assessment of attributes with CF included of nominal scores of numerous movies. An intricate technique of collaborative filtering is efficient for the drive of recommendation throughout assessment and to associate performance of the arrangement with the addition of attributes from user assessments and deprived of the attributes from user reviews. This technique performs overall improved methodology, which is merely built on nominal ratings and movie attributes like movie genres.

Sentiment analysis in various languages has been a research hotspot with many applications. However, sentiment resources (e.g., labeled corpora, sentiment lexicons) of different languages are unbalanced in terms of quality and quantity, which arouses interests in cross-lingual sentiment analysis aiming at using the resources in a source language to improve sentiment analysis in a target language. Nevertheless, many existing cross-lingual related works rely on a certain machine translation system to directly adapt the labeled data from the source language to the target language, which usually suffers from inaccurate results generated by the machine translation system. On the other hand, most sentiment analysis studies focus on document-level sentiment classification that cannot solve the aspect dependency problem of sentiment words. For instance, in the reviews on a cell phone, long is positive for the lifespan of its battery, but negative for the response time of its operating system. To solve these problems, this paper develops a novel Cross-Lingual Joint Aspect/Sentiment (CLJAS) model to carry out aspect-specific sentiment analysis in a target language using the knowledge learned from a source language. Specifically, the CLJAS model jointly detects aspects and sentiments of two languages simultaneously by incorporating sentiments into a cross-lingual topic model framework. Extensive experiments on different domains and different languages demonstrate that the proposed model can significantly improve the accuracy of sentiment classification in the target language.

Sentiment Analysis is a process of computing, identifying, and categorizing opinion from a piece of text and discovering if the writer’s attitude toward a particular topic or product is positive or negative. In this paper, we are doing Sentiment Analysis of Twitter Data which are mixed code (English and Hindi). Our model works on different types of Twitter input data, namely Mixed script—the tweet is written in combination of Native script and Roman script, Code-Mixed script—in this type, the tweet is in native language but Roman script was used to write it, and Native script—here, the Regional language tweet is written in Native script. We are developing our model in Jupyter Notebook which is an open-source web application software. Our main aim is to increase the efficiency of the Machine Learning model, so we are building the machine learning model with pipeline techniques and Hyperparameter Tuning technique. We are using the Twitter Training Dataset to train our model. Initial step is to do the Text pre-processing thereby removing the unwanted noise in the data. We are using the cross-validation method to overcome the underfitting and overfitting problems. We build the model with different classifiers, namely Logistic Regression, Decision Tree, Random forest, Multinomial Naive Bayes, Support Vector and vectorizers, namely count and term frequency-inverse document frequency (TFIDF). We tried the different combination of parameter tuning and found the best parameter which gives the maximum accuracy. After the analysis and voting classifier output, we found that the combination of TFIDF vectorizer and Random Forest Classifier pipeline gives the maximum accuracy of 96.57% and Predicts the tweets in real time as Positive or Negative.