A Comprehensive Review of Sentiment Analysis on Indian Regional Languages: Techniques, Challenges, and Trends

Sentiment analysis (SA) is the process of understanding emotion within a text. It helps identify the opinion, attitude, and tone of a text categorizing it into positive, negative, or neutral. SA is frequently used today as more and more people get a chance to put out their thoughts due to the advent of social media. Sentiment analysis benefits industries around the globe, like finance, advertising, marketing, travel, hospitality, etc. Although the majority of work done in this field is on global languages like English, in recent years, the importance of SA in local languages has also been widely recognized. This has led to considerable research in the analysis of Indian regional languages. This paper comprehensively reviews SA in the following major Indian Regional languages: Marathi, Hindi, Tamil, Telugu, Malayalam, Bengali, Gujarati, and Urdu. Furthermore, this paper presents techniques, challenges, findings, recent research trends, and future scope for enhancing results accuracy

A Survey of Algorithms Involved in the Conversion of 2-D Images to 3-D Model

Since the advent of machine learning, deep neural networks, and computer graphics, the field of 2D image to 3D model conversion has made tremendous strides. As a result, many algorithms and methods for converting 2D to 3D images have been developed, including SFM, SFS, MVS, and PIFu. Several strategies have been compared, and it was found that each has pros and cons that make it appropriate for particular applications. For instance, SFM is useful for creating realistic 3D models from a collection of pictures, whereas SFS is best for doing so from a single image. While PIFu can create extremely detailed 3D models of human figures from a single image, MVS can manage complicated situations with varied lighting and texture. The method chosen to convert 2D images to 3D ultimately depends on the demands of the application