Saksham Garg

Bio: Saksham Garg is an academic researcher from Manipal University. The author has contributed to research in topics: Customer satisfaction & Social media. The author has an hindex of 1, co-authored 1 publications receiving 2 citations.

Assigning Sentiment Score for Twitter Tweets

Abstract: Customer satisfaction has become a part of many business. Unlike in the past, companies just do not rely on pure advertisement to make their product more desirable. Their prime concern now has turned towards customer satisfaction. Similarly, people are more curious to know about the current popular opinion on events happening around the world and information about the favorite celebrities, favorite product, etc. People have turned towards social media to share their experiences and views about products as well as other people. The current work aims at using this as a base for developing a system that perceives the opinion of people about a specific product or a person. Till now, there is a lot of research that has been done in this topic. Various papers have showed different strategies to enhance sentiment analysis. In this paper, we have worked on improving the algorithms so that the sentiment conveyed can be classified in the appropriate class it belongs to.

Effective Text Data Preprocessing Technique for Sentiment Analysis in Social Media Data

Abstract: In the big data era, data is made in real-time or closer to real-time. Thus, businesses can utilize this evergrowing volume of data for the data-driven or information-driven decision-making process to improve their businesses. Social media, like Twitter, generates an enormous amount of such data. However, social media data are often unstructured and difficult to manage. Hence, this study proposes an effective text data preprocessing technique and develop an algorithm to train the Support Vector Machine (SVM), Deep Learning (DL) and Naive Bayes (NB) classifiers to process Twitter data. We develop an algorithm that weights the sentiment score in terms of weight of hashtag and cleaned text. In this study, we (i) compare different preprocessing techniques on the data collected from Twitter using various techniques such as (stemming, lemmatization and spelling correction) to obtain the efficient method (ii) develop an algorithm to weight the scores of the hashtag and cleaned text to obtain the sentiment. We retrieved N=1,314,000 Twitter data, and we compared the popularity of two products, Google Now and Amazon Alexa. Using our data preprocessing algorithm and sentiment weight score algorithm, we train SVM, DL, NB models. The results show that stemming technique performed best in terms of computational speed. Additionally, the accuracy of the algorithm was tested against manually sorted sentiments and sentiments produced before text data preprocessing. The result demonstrated that the impact produced by the algorithm was close to the manually annotated sentiments. In terms of model performance, the SVM performed better with the accuracy of 90.3%, perhaps, due to the unstructured nature of Twitter data. Previous studies used conventional techniques; hence, no precise methods were utilized on cleaning the text. Therefore, our approach confirms that proper text data preprocessing technique plays a significant role in the prediction accuracy and computational time of the classifier when using the unstructured Twitter data.

Analysis of Public Sentiment on COVID-19 Vaccination Using Twitter

Abstract: Social media has become a vital platform for individuals, organizations, and governments worldwide to communicate and express their views. During the coronavirus disease 2019 (COVID-19) pandemic, social media sites play a crucial role in people communicating, sharing, and expressing their perceptions on various topics. Analyzing such textual data can improve the response time of governments and organizations to act on alarming issues. This study aims to perform sentiment analysis on the subject of COVID-19 vaccination, perform temporal and spatial analyses of the textual data, and find the most frequently discussed topics that may help organizations bring awareness to those topics. In this work, the sentiment analysis of tweets was performed using 14 different machine learning classifiers and natural language processing (NLP). Lexicon-based TextBlob and Vader are used for annotating the data. A natural language toolkit is used for preprocessing of textual data. Our analysis observed that unigram models outperform bigram and trigram models for all four datasets. Models using term frequency-inverse document frequency (TF-IDF) have higher accuracy than models using count vectorizer. In the count vectorizer class, logistic regression has the best average accuracy with 91.925%. In the TF-IDF class, logistic regression has the best average accuracy of 92%; logistic regression has the highest average recall, F1-score, and ten cross-validation scores, and a ridge classifier has the highest average precision. The unigram models show a standard deviation (SD) of less than 1 for all classifiers except for the Gaussian Naïve Bayes showing 1.18. The experimental results reveal the dates and times in which most positive, negative, and neutral tweets are posted.

Sentiment Analysis to Measure Public Response to Online Education During Coronavirus Pandemic

Abstract: Corona virus (Covid-19) has infected the world with huge impacts and changes that were not imagined, in large sectors such as the economy and education. The education sector is considered an important sector for all societies due to its desired impact on the future of generations. The increasing and rapid spread and precautionary measures have led to the search for suitable alternatives for the continuation of education to ensure that students receive appropriate education and are not affected scientifically or psychologically in the event of dropping out of education. This study seeks to examine the extent to which the community accepts distance learning as a precaution by employing the sentiment analysis of Twitter’s tweets as one of the most popular social media. This method is considered effective due to its ability to access the community’s tweets quickly and at a low cost.

Feature Based Opinion Mining for Mobile Reviews

Abstract: Item surveys or client criticism has become an ideal platform for sellers to promote their goods and clients to expand their knowledge and purchase wisely. As the online system of buying and selling is expanding, a measure of client audits additionally has been expanded undeniably. Thus it is an intense assignment for retailers just as clients to peruse the surveys related with the item. Notion examination settle this issue by looking over free content audits and giving the assessment synopsis. Highlight based supposition based investigation strategies expands the granularity of notion of examination by dissecting extremity related with highlights in the given free content. The main objective of this work is to plan a system that predicts extremity and to plan a score which determines the stratum of extremity. Resulting component-level scores are summed up as per the clients’ need of interest.

Sentiment Analysis of National Eligibility-Cum Entrance Test on Twitter Data Using Machine Learning Techniques

Abstract: People around the world use social media to communicate and share their perceptions about a variety of topics. Social media analysis is crucial to interacting, distributing, and stating people's opinions on various topics. Governments and organizations can take action on alarming issues more quickly with the help of such textual data investigation. The key purpose of this effort is to perform sentiment analysis of textual data regarding National Eligibility-cum Entrance Test (NEET), perform classification and determine how people feel about NEET. In this study, 11 different machine learning classifiers were used to analyze tweet sentiment, along with natural language processing (NLP). Tweepy is the python library which is used to get user opinion about NEET Exam. Annotating the data is accomplished using TextBlob and Vader. Text data is pre-processed with a natural language toolkit. The dataset downloaded from Twitter shows that unigram models perform well compared to bigram and trigram models. TF-IDF models are more accurate than count vectorizer which is based on word frequency. classifier achieves an average accuracy of 92%. Perceptron also receives the uppermost average accuracy of 91%. According to the data from the experiment, most people have a neutral opinion of NEET.