What is Sentiment Analysis?
Sentiment analysis is one of the most important components of text mining. Also termed as opinion mining, it involves careful analysis of people’s opinions, sentiments, attitudes, appraisals, and evaluations. This is accomplished by examining large amounts of unstructured data obtained from the Internet on the basis of positive, negative, or neutral view of the end user.
Table of Content
Sentiment analysis involves the analysis of following sentences:
- Facts: Product A is better than product B.
- Opinions: I don’t like A. I think B is better in terms of durability.
Similar to Web analysis, specific queries are applied in sentiment analysis to retrieve and rank relevant content. However, sentiment analysis also differs from Web analysis in certain factors.
It is possible to determine from a sentiment analysis that whether the content expresses an opinion on the topic, and also whether the opinion is positive or negative. Ranking in Web analysis is done on the basis of the frequency of keywords.
On the other hand, ranking in sentiment analysis is done on the basis of polarity of the attitude.
With the widespread use of Web 2.0 technologies, a huge volume of opinioned data is available on the social media. People using social media put their reviews and comments about products used and also share their feedback, opinions and experiences with others in their network. These reviews and feedback are utilised by organisations to improve and upgrade their products and services, and enhance their brand equity.
Sentiment analysis applies other domains such as linguistics, digital technologies, text analysis tools, artificial intelligence, and Natural Language Processing (NLP) for identification and extraction of useful information. This greatly influences various domains ranging from politics and science to social science.
The process of sentiment analysis begins by tagging words using Parts of Speech (POS) such as subject, verb phrase, verb, noun phrase, determiner, and prepositions. Defined patterns are filtered to identify their sentiment orientation. For example, ‘beautiful rooms’ has an adjective followed by noun.
The adjective ‘beautiful’ indicates a positive perspective about the noun ‘room’. At this stage, the emotional factor in the phrase is also examined and analysed. After that, an average sentiment orientation of all the phrases is computed and analysed to conclude if a product is recommended by a user.
Following parameters may be applied to classify the given text in the process of sentiment analysis:
- Polarity, which can be positive, negative, or neutral
- Emotional states, which can be sad, angry, or happy
- Scaling system or numeric values
- Subjectivity or objectivity
- Features based on key entities such as durability of furniture, screen size of cell phone, and lens quality of camera
Automated sentiment analysis is still evolving as it is difficult to interpret the conditional phrases used by people to express their sentiments on social media. For example, ‘if you don’t like A, try B’. In this sentence, the user clearly shows his/her positivity towards B, but doesn’t indicate clear views about A. After removing ‘if’, the first clause clearly indicates negativity towards A.
Sentiment Analysis Tool
However, sentiment analysis employs various online tools to effectively interpret consumer sentiments. Some of the online tools are listed as follows:
Used to measure success of a Website on Twitter. It tracks the occurrence of given and related keywords, website name, and website URL in tweets.
Applied to improve search engine ranking of a website. It tracks tweets that link back to a website.
Used to locate tweets that are important for a website. It can be used to stay in touch with the customers and consumers, and respond to their queries and suggestions in real time.
Used to send timely updates or alerts for the topics of interest.
Designed specifically for Pinterest, a content sharing website. This tool helps in tracking data and scheduling and organising pins (denote the updates in Pinterest) in advance.
Apart from these, some other sentiment analysis tools include Social Mention, AlertRank Sentiment Analysis, and Twitter Sentiment Analysis. The business organisations can apply specific tools as per their requirement and sentiment analysis needs.