Abstract: Sentimental data and Trend Analysis

We develop sentimental data and trend analysis to analyze the Positive, Negative and Moderate comments from the social networks. Millions of users share their opinions on Social Networks. We making it a valuable platform for tracking and analyzing public sentiments. Such tracking and analysis can provide critical information for decision making in various domains. Therefore, this application is much useful in all sectors like Politics, Cini industries, Manufacturing industries, collecting statistical data, etc.

Previous research mainly focused on modeling and tracking public sentiment. In addition, we move one step further to interpret sentiment variations. Therefore, We observed that emerging topics (named foreground topics) within the sentiment variation periods are highly related to the genuine reasons behind the variations.

This Proposed work Sentimental data and Trend Analysis will analyze the public sentiment variations in microanalysis methods called Pattern recognition. we use Clustering and Classification methods for grouping and ranking. It contains an inbuilt data dictionary called ANN (Artificial Neural Network). For instance, the user can upload training data set. The proposed models applied to other tasks such as finding topic differences between two sets of documents.

We propose a sentimental data analysis model using Pattern Recognition. All sentiments like positive, negative and moderate feedbacks can be calculated here. These foreground topics can give potential interpretations of the sentiment variations from the data set.

The data set contains the previously posted comments by an individual or by an organization. The pattern recognition method and classification technique will analyze all the comments. As mentioned above, all the sentiments in the comments will be separated. And non-hitting sentences will be considered as moderate comments. All the above results will be stored in the database and represented in a graphical format.

In Conclusion, from the uploaded dataset, the system will analyze the comment. The result will show the number of positive comments and negative comments. Along with the most dominating keywords in the dataset in chart and graph.

Keywords :

Data Mining, Social network comment analysis, sentimental data analysis, positive and negative comments, Clustering, Classification, facebook, twitter, ranking, comments analysis, Performance, ANN (Artificial Neural Network), Chart, Graph, Web application, MCA projects, MSc Projects, CSE Projects, IT Projects, Engineering projects, data sets.

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