Sentimental analysis
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This document discusses sentiment analysis, which analyzes texts like reviews and blogs to determine the attitude or opinion expressed. It goes through the phases of preprocessing data, extracting key features, and classifying the sentiment. The purposes are to help customers and organizations by analyzing feedback data. Limitations include not providing alternative suggestions, and an inability to formulate a new combined algorithm. Algorithms used are Naive Bayes, fuzzy conventional method, and hidden Markov models.
Sentimental analysis
- 2. An Overview Sentiment analysis is a part of Natural Language Processing where attitudes or opinions of user are undermined The texts from reviews or Blogs is processed to get an accurate description of how the writer feels regarding the subject.
- 3. Phases Pre processing phase: the data entered by user is first cleaned to reduce noise so that key words could be analyzed Feature Extraction Phase: In this phase the key words are given a token and are put under analysis Classification Phase: Now we have all the data waiting to be classified. Based on different algorithms they are put under Category of happy, sad etc.
- 4. Purpose With many websites enabling review option for products, there is an incremental increase in data in natural language. Analysis of this data is beneficial for both Customers as well as organizations to keep track of product Activity. Data from feedback forms can be processed to find sentiment of people regarding the teacher/subject/college.
- 5. Limitation This project does not provide alternate suggestions for the products to which customers are not satisfied with. A combined new algorithm could not be formulated to put forward the best way to evaluate in future.
- 6. Algos Used Naïve Bayes(Conventional) Theorem: This is the conventional theorem that collects term frequency and classify data based on that information. Fuzzy Conventional Method: It provides a degree of associativity to each key word with respect to whole document. Hidden Markov Model: This is an algorithm used in predictive analysis. Based on current mood it can predict what the probability of future sentiment could be.
- 7. Screenshots