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Sentiment analysis is process of extracting information from user"s opinions. Every person shares his or her information on social network sites, blogs, product review websites and webforums. Thus, we get familiar with the thinking of the other people. People"s thinking that provides an information that helps in decision making process. This Paper describe different applications of sentiment analysis, techniques and challenges of sentiment analysis.

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Classification of Web pages is one of the challenging and important task as there is an increase in web pages in day to day life provided by internet. There are many ways of classifying web pages based on different approach and features. This paper explains some of the approaches and algorithms used for the classification of webpages. Web pages are allocated to predetermined categories which is done mainly according to their content in Web page classification. The important technique for web mining is web page classification because classifying the web pages of interesting class is the initial step of data mining. The agenda of this paper is first to introduce the concepts related to web mining and then to provide a comprehensive review of different classification techniques.

Cross-modal retrieval(CMR) refers to the task of retrieving semantically related items across different modalities. For example, given an image query, the task is to retrieve relevant text descriptions or audio clips. One of the major challenges in CMR is the modality gap, which refers to the differences between the features and representations used to encode information in different modalities. To address the modality gap, researchers have developed various techniques such as joint embedding, where the features from different modalities are mapped to a common embedding space where they can be compared directly. Binary-valued and real-valued representations are two different ways to represent data. A binary-valued representation is a type of discrete representation where data is represented using either 0 or 1. Real-valued representation, on the other hand, represents each item as a vector of real numbers. Both types of representations have their advantages and disadvantages, and researchers continue to explore new techniques for generating representations that can improve the performance of CMR systems. First time, the work presented here generates both the representations and comparison is made by performing experiments on standard benchmark datasets using mean average precision (MAP). The result suggest that real-valued representation outperforms binary-valued representation in terms of MAP, especially when the data is complex and high-dimensional. On the other hand, binary codes are more memory-efficient than real-valued embedding, and they can be computed much faster. Moreover, binary codes can be easily stored and transmitted, making them more suitable for large-scale retrieval tasks.

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