In recent years, it is seen that the opinion-based postings in social media are helping to reshape business and public sentiments, and emotions have an impact on our social and political systems. Opinions are central to mostly all human activities as they are the key influencers of our behaviour. Whenever we need to make a decision, we generally want to know others opinion. Every organization and business always wants to find customer or public opinion about their products and services. Thus, it is necessary to grab and study the opinions on the Web. However, finding and monitoring sites on the web and distilling the reviews remains a big task because each site typically contains a huge volume of opinion text and the average human reader will have difficulty in identifying the polarity of each review and summarizing the opinions in them. Hence, it needs the automated sentiment analysis to find the polarity score and classify the reviews as positive or negative. This article uses NLTK, Text blob and VADER Sentiment analysis tool to classify the movie reviews which are downloaded from the website www.rottentomatoes.com that is provided by the Cornell University, and makes a comparison on these tools to find the efficient one for sentiment classification. The experimental results of this work confirm that VADER outperforms the Text blob.
Over 322 million people worldwide are affected by depression, the leading cause of disability. Major depressive disorder (MDD) is the most frequent mental disorder and contributes significantly to the global disease burden. Current depression diagnoses, on the other hand, are beset by issues such as patient denial, clinical experience, and self-report bias. Early detection of depression can assist in lessening or even eliminating its detrimental effects. To help the conventional diagnostic approaches in psychiatry, there has been much research into automated depression prediction in recent years. Automated depression identification based on machine learning techniques can help disorder analysts diagnose depression more effectively. The literature on depression has proven that the acoustic features of a depressed individual vary from a normal individual. This paper aims to identify the minimal acoustic features that can be used to detect depression accurately and propose a majority voting classifier for detecting depression (MVCDD). MVCDD is designed with the base classifiers, including Random Forest Classifier (RFC), Support Vector Classifier (SVC), and Logistic Regression (LR). The experiments are performed on the data collected from the students of Government Polytechnic, Masabtank, Hyderabad, India.
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