The strength of sentiment or opinion is typically Polarity determines emotionsĮxpressed in a sentence, emotions are closely related to Machine learning algorithms to analyze their thoughts using How they feel and somewhat solve their dilemma byĬonsulting doctors and help groups. Unreasonable actions like suicide? However, if we could know Usually, a lot of time people don't even know Near and dear ones, they usually express themselves using And people don’t usuallyĮxpress themselves to their loved ones, this creates a sense of In this rapid, non-stop world everything and everyone goes onĭramatically and enthusiastically. Furthermore, a contrast has been presented between four different kernels of SVM: RBF, linear, polynomial and sigmoid. This paper also highlights a comparison between the results obtained by exploiting the following machine learning algorithms: Naive Bayes and Support Vector Machine and an Artificial Neural Network model: Multilayer Perceptron. Besides taking additional preprocessing measures like the expansion of net lingo and removal of duplicate tweets, a probabilistic model based on Bayes' theorem was used for spelling correction, which is overlooked in other research studies. This paper is a thorough effort to dive into the novel domain of performing sentiment analysis of people's opinions regarding top colleges in India. While Twitter data is extremely informative, it presents a challenge for analysis because of its humongous and disorganized nature. With the advent and growth of social media in the world, stakeholders often take to expressing their opinions on popular social media, namely Twitter. In today's world, opinions and reviews accessible to us are one of the most critical factors in formulating our views and influencing the success of a brand, product or service. This paper also focuses on the efficiency analysis of three machine learning algorithms-Naïve Bayes, Random Forest, Support Vector Machine (SVM).

That way, we can contribute to making the world a better place to live for women. The main focus of this research paper is to find out the least safe state in our country so that we can educate people in that area to promote the safety of women with the help of social media. Hence by reviewing these classified tweets, we can identify the places which are less safe for women. We can categorize their opinions by using tweets. Twitter gives an excellent feature for women to express their views about what they feel while they travel or go out. To control their paranoia in this unsafe society, we need to manage and handle the potentially detrimental situations that may lead to such unfortunate events. They still feel like men are eyeing them when they travel or roam outside. Women from various parts of the world always experience a lot of harassment, starting from stalking, passing vulgar comments, and leading to sexual assault.

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