• Nisha Yadav
• Dr. Banita

Natural Language Processing and machine learning models has widely used in today's field. To analyze the various emotions and to categorize the various opinions of all the users, sentiment analysis has been used. In this study, Twitter database is used to calculate the opinions of all the users which are using Twitter. Twitter is basically the social platform which is used to communicate at faster mode. With the help of Twitter, we can easily communicate within a couple of seconds. We can share the data either in audio, video or textual format. Support Vector Machine has also been used in this study to categorize and to analyze the whole data set along with the performance which make the proposed work much accurate. The opinions are segregated into three parts, that is positive, negative and neutral aspect. The sentiment analysis basically divides the performance will be analyzed on 2 basic aspect that is semantic analysis and opinion mining. The basic approach for analyzing the data is firstly we use the various data set from the Twitter database. Then the data preprocessing will be done which will help to figure out and filter the various noise. After that Linguistic Data Processing with the help of Natural Language Processing is used. It basically mentioned the polarity of all the sentiments. And then we will apply in this study machine learning algorithm and it lasts after applying the machine learning algorithm, the result will be analyzed. the whole sentence into number of words and then words get segregated into tokens.

Twitter Analysis, Opinion based, Sentiment Score, Matrix Analysis

"EVALUATING THE SENTIMENT SORE WITH THE USE OF MATRIX REPRESNTATION IN SENTIMENT ANALYSIS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 2, page no.a334-a338, February-2024, Available :http://www.jetir.org/papers/JETIR2402041.pdf

"EVALUATING THE SENTIMENT SORE WITH THE USE OF MATRIX REPRESNTATION IN SENTIMENT ANALYSIS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 2, page no. ppa334-a338, February-2024, Available at : http://www.jetir.org/papers/JETIR2402041.pdf