Volume 7, Issue 11, November – 2022 International Journal of Innovative Science and Research Technology ISSN No:-2456-2165 IJISRT22NOV190 www.ijisrt.com 761 Twitter Sentiment Analysis with Textblob Nevil Gajera 1 Department of Computer Science and Engineering, Devang Patel Institute of Advance Technology and Re- search (DEPSTAR), Faculty of Technology and Engineer- ing (FTE), Charotar University of Science and Technology (CHARUSAT) Kishan Chanchad Department of Computer Science and Engineering, Devang Patel Institute of Advance Technology and Re- search (DEPSTAR), Faculty of Technology and Engineer- ing (FTE), Charotar University of Science and Technology (CHARUSAT) Sneh Vora Department of Computer Science and Engineering, Devang Patel Institute of Advance Technology and Research (DEPSTAR), Faculty of Technology and Engineering (FTE), Charotar University of Science and Technology (CHARUSAT) Abstract:- In the World, many social media sites exist like Twitter, Instagram, Facebook, Snapchat, etc. and data posted by people on these social media sites are in- creasing quickly that containing audio, video, text, and images. People use this site to share their thoughts and opinion and sometimes share their opinion and thoughts towards any company. For this, we have chosen twitter and applied sentiment analysis. In this paper, we discuss a method of data extraction through API, data cleaning, and use text blob python library for sentiment analysis. Keywords:- Twitter, sentiment, opinion mining, social me- dia, natural language processing, sentiment analysis. I. INTRODUCTION Social Media Websites are carrying a sea of data. So- cial Media sites have given a right to speak to every person who can access or use them. Twitter is used by a large num- ber of people to write their emotions, opinions about their daily life, and a company or any organization reviews and opinions. Twitter is challenging because its users have to express their views in one or two key sentences and it can be seen as a good reaction to what is happening around the world. Sentiment analysis automates the extraction or classi- fication of sentiment and views using text analysis, natural language processing, and computational approaches. This sentiment analysis benefits many fields like Customer in- formation, Marketing field, books, mobile application, So- cial media, and websites. Many companies hire analysts who have a job to extract the emotions of people behind these posts or tweets. This helps businesses to get a good review about a product or service which helps them know public opinion and in addition, they make a better product in the future. In this project, we use the python text blob library for text classification. There are two ways to extract tweets us- ing Twitter's official API and data scraper. For this project, we preferred API for collecting datasets. II. LITERATURE REVIEW In October 2017 Kirti Huda, Mrunmayee Deshpande, and Neshat Karim gave information on Classification Tech- niques for Sentiment Analysis of Twitter tweets data. In classification, there are mainly three techniques Naive Bayes, M E, and SVM. The author uses a pattern-based technique for feature extraction. In this, for feature extrac- tion, The n-gram algorithm is used, which assigns a priority to each word that needs to be classified. In the last step of classification, they use a support vector machine (SVM). After the classification, they conclude that the accuracy of the E- Pattern-based algorithm is given a more accurate re- sult than pattern based algorithm, and in terms of time also E-pattern based algorithm takes less execution time. In June 2017 Shivam Singh, Sonal Agarwal, And Sak- shi Agarwal proposed a Real-Time Twitter Sentiment Anal- ysis. In this research, they use Hadoop with natural language processing. 1st is the Ingestion of tweets into HDFS in this Tweets are ingested from Twitter streaming using Twitter 4j API. 2nd is Post Processing, Construction of n- grams, and Spelling correction. 3rd is Query processing using HIVE Once the tweets are ingested into HDFS. Excel uses the ODBC driver to get the processed data in the form of graphs, geographical location, and charts-based data because the culture and diversity of a location matter very much. In February 2018 Sahar A. El_Rahman, Feddah Alhu- maidi AlOtaibi, And Wejdan Abdullah AlShehri proposed a Sentiment Analysis of Twitter Data. The author used senti- ment analysis to classify English tweets about two famous restaurants that are McDonald's and KFC. In this method they use some packages and libraries, some packages are Twitter, R0Auth, and word cloud, after preparing tweets using an unsupervised learning algorithm they used a lexi- con- based model used to classify Twitter tweets. To train the model they use different supervised algorithms: Naive Bayes, SVM, random forest, decision tree, and maximum entropy. For Accuracy They use Recall, Precision, F-score, And Cross- validation. In September 2019 Brahmananda Reddy, D.N.Vasundhara, and P. Subhash proposed Sentiment re- search on Twitter data. This system was completed in seven stages. In this system, they overcome the drawbacks for bet- ter understanding the emotions they classified emotions into 7 categories ex. Strongly Positive, Positive, Weakly posi- tive. Instead of static data, they use real-time data using Twitter API by giving a username or hashtag and They can look at a specific person's tweets or hashtags. In this re- search, the author uses a Naïve bye classifier and they use video games review data sets for training and testing. The Bayes theorem is used in the Naive Bayes technique, which uses a probabilistic learning function.