In today's digital world, millions of individuals are linked to one another via the Internet and social media. This opens up new avenues for information exchange with others. Sentiment analysis (SA) has gotten a lot of attention during the last decade. We analyse the challenges of Sentiment Analysis (SA) in one of the Asian regional languages known as Marathi in this study by providing a benchmark setup in which we first produced an annotated dataset composed of Marathi text acquired from microblogging websites such as Twitter. We also choose domain experts to manually annotate Marathi microblogging posts with positive, negative, and neutral polarity. In addition, to show the efficient use of the annotated dataset, an ensemble-based model for sentiment analysis was created. In contrast to others machine learning classifier, we achieved better performance in terms of accuracy for ensemble classifier with 10-fold cross-validation (cv), outcomes as 97.77%, f-score is 97.89%.

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Sentiment analysis (SA) is information retrieval and computational linguistics that expresses sentiment about a specific document. Recently, research on SA has received greater interests due to the vast expansion of internet data. The comments and reviews posted online creates difficulties to the researchers, because the comments posted are written in unstructured formats with informal expressions, languages and possibly mixed languages. Most of the works on SA have been focusing only on English language but with respect to Indian languages, few works are done, especially on Hindi, Telugu, Bengali, and so forth. SentiWordNet is a useful lexical resource for determining the sentiment of a document or piece of text. There is currently no lexical resource for the Marathi language used for sentiment analysis. Therefore, this proposed research presents SA in one of the languages of Indian origin, that is, Marathi. In this paper, a new method is proposed for the development of a Marathi SentiWordNet (M-SWN), synset-based expansion approach using Marathi WordNet, which consists of Positive, Negative, and Neutral Polarity score, mapped from Hindi-SentiWordNet (H-SWN).Finally, sentiment analysis of movie reviews is carried out using M-SWN using Bi-LSTM based SVM classifier model.

Objectives:To examine the quality of healthcare services and the features (aspects) of those services, as well as the variation in those services' quality over a time. Methods: The study presents a method which includes firstly by collecting patient feedback data from the internet, and then follows preprocessing, extracting aspects of healthcare, and finally performing aspectbased sentiment analysis of healthcare. This aspect-based sentiment analysis is created to determine the pattern of aspect in a sentence using the BERT model. Healthcare services and their aspect-quality service analysis are performed here date-wise, i.e., timestamp-wide. A total of 69 physician are selected to collect the feedback and analyzed the feedback using an aspect-based sentiment analysis technique. Findings: The quality of healthcare services is frequently changing. In healthcare, for example, sometimes there is good quality service and sometimes there is worst quality service. All 69 physicians' total of 300 sentences with aspect-based sentiment scores are extracted separately after preprocessing and normalization. The aspect-wise results are shown in percentages. After that, the extracted aspect-wise percentages are shown as per date. Out of a total of 69 physicians, sample of D9, i.e., Doctor 9, patient feedback results, are shown in this paper. Novelty: This study made the aspect-based sentiment analysis score, which demonstrates the datewise, i.e., timestamp-wide variation in healthcare services. Previous research has made healthcare predictions using feedback ratings; no study has yet performed a date-wise analysis. The features, such as diagnosis, treatment, cleanliness, appointment, advice, medicine, staff service, etc., are included for analysis.