Sentiment Analysis
The word opinion mining is utilized in many different applications for sentiment analysis. Its purpose is to check the opinion of the people on the product’s benefits and product’s characteristics (features). Essentially, an evaluation of opinion is a kind of common approach used to determine whether or not people are interested in the product. The emotions or feelings of people are expressed textually. These aspects are obtained from different sites or mobile applications. (Duygulu et al., 2002)
Analysis of sentiment is used to keep in touch or show the feelings of people. People give product’s positive and negative opinion for the services of item. When making any choice for purchase the product, these opinions are very valuable for the customer (Zhang et al., 2013). It is very difficult to correctly interpret the right view in the sense of large textual data set and unstructured data sets. To identify the characteristics of an unstructured dataset, it is essential to structure a productive technique (Yu et al., 2010).
Preprocessing, feature extraction and classification are three main parts and analysis methods for sentiment analysis (Xu et al., 2013). Some association only works with positive and negative text. They skip the neutral text recognition. Such texts use the binary classification limits. Several researchers introduce polarity problems for perform three classes (Ren & Wu, 2013). For entropy and SVM classifiers, the neutral classes, presentation etc. are very essential. This improves the accuracy of overall classification. These principles ought to be considered by the neutral classes for the execution. The first algorithm understands the concept of neutral language and extracts people’s remaining opinions. In simply single phase, this algorithm completes the three-level classification (Poria et al., 2013).
In every class, in the next methodology, the probability and distribution are calculated (Garcia-Moya et al., 2013). When the data is the most neutral with the variance between positive and negative outcomes, it becomes more difficult to implement this methodology (Cheng et al., 2013). Sentiment analysis is used in some various ways. It is useful for marketers to measure the credibility and accomplishment of some new product dispatch and to see what edition of the product is in demand and show the new product's famous highlights (Hai et al., 2013)
The below are some of the key features of tweets:
- •
Message Length
- •
Writing technique
- •
Availability
- •
Topics
- •
Real time
Benefits of Using Sentimental Analysis
Sentiment analysis has many benefits given to different business and organization. It helps and gives their business valuable insights that how people feel about their service and brand. It will allow user to identify potential of their products to create more influence on social media. Hence, it will be used to identify negative threads that are emerging online regarding your business, thereby allowing you to be practical in dealing with it more quickly. Some of the benefits are as follow:
- •
Marketing strategy
- •
Enlarge product quality
- •
Improve customer service
- •
Crisis management