An Overview of Different techniques of Sentiment Analysis

 Sentiment Analysis is the process of deducing the attitude of a piece of text as either positive, negative or neutral. It is used in many different industries to understand public opinion on everything from products to political candidates. There are a few different ways to perform sentiment analysis, each with their own advantages and disadvantages. In this post, we'll take a look at the most common techniques used in sentiment analysis and see how they work. Stay tuned for future posts where we will explore some specific applications of sentiment analysis!

One of the most common methods for performing sentiment analysis is to use a lexicon, or dictionary, of known positive and negative words. The sentiment of a text can then be calculated by counting the number of positive and negative words it contains. This method is simple to implement but has several drawbacks. First, it only considers the words in the text and not their context. This can lead to errors if a word has multiple meanings (e.g., "bass" can refer to a type of fish or a low frequency sound). Second, the lexicon must be manually created and updated, which is time-consuming and requires domain expertise. Finally, this method is not very effective at handling negation (e.g., "not good").

Another popular method for performing sentiment analysis is to use a machine learning algorithm. This approach requires a training dataset of texts with known sentiment labels (e.g., positive, negative, neutral). The machine learning algorithm is then able to learn the features that distinguish between the different sentiment labels. This approach is more flexible than the lexicon-based approach as it can take into account the context of words as well as negation. However, it requires a large training dataset and can be time-consuming to train the machine learning algorithm.

A third method for performing sentiment analysis is to use a rule-based approach. This approach uses a set of rules, defined by humans, to label the sentiment of a text. For example, a rule might state that any text containing the word "good" is positive, while a text containing the word "bad" is negative. This approach is relatively simple to implement but can be error-prone as it does not take into account the context of words or negation.

In conclusion, sentiment analysis is a process used to understand the attitude of a piece of text. There are three main methods for performing sentiment analysis: using a lexicon, using a machine learning algorithm, and using a rule-based approach. Each method has its own advantages and disadvantages. In future posts, we will explore some specific applications of sentiment analysis!