Cover image
project image

Rating Prediction Based On Social Sentiment

neeraj

Verified

Price : $ 70

View : 49      Download : 3

Product Discription

04-December-2018

In the current web scenario, web contents are growing drastically and huge in size. Analyzing and finding desired knowledge from the huge content is possible with the help of data mining. Data mining is an essential part of current applications like e-commerce, web search and others. In web search and ecommerce applications, the content recommendation is probably based on the ratings and popularity. Sometimes the ratings are not explicitly given, thus it decreases the product or content reach. Opinion or sentiment analysis is trending in all type of social networks and e-commerce applications. Sentiment analysis is helpful for the product rating prediction based on the reviews. This paper gathers a list of approaches and methods used to find the sentiment from the customers review. Sentiment analyses are vital process to most web recommendation activities because they are major influencers of the others interest. 

 

The proposed method is performed in four stages: The first stage is Pre-processing where removing of unwanted words that not deliver any knowledge. Second stage, product name identification in the review. Third is to calculate the rating score of the review.  Fourth is to calculate the user interpersonal influence by using social network sites like (facebook, twitter, etc.). Finally predict the rating of the user by using individual score, inter personal score and product market value, fig 2 represent all set of steps.

 

In previous work the products are classify by identifying the quotes manually as from which side it belong base on the positive or negative words. But this can be done by including a dictionary of the lexicons. With the help of this overall accuracy will be increase and the time of product separation will also be reduce.

Project Sample Image

Other Detail

Software Requirement :   MATLAB

Hardware Requirement :   • Intel Processor 2.0 GHz or above. • 2 GB RAM or more. • 160 GB or more Hard Disk Drive or above.


Application :  


Project Attachement

PDF IEEE Base paper
Doc Complete Project word file document
Read me Complete read me text file
Source Code Complete Code files