Sentiment analysis tools to explore the customers' emotions within the texts
Sentiment Analysis aims at obtaining meaningful information and semantics from a text, by using natural processing techniques and determining the writer's attitude, whether positive, negative, or neutral.
If you are positive about things, you are hopeful and confident.
Think of the good aspects of a situation rather than the bad ones
Showing satisfaction level of experience by positive adjectives
Indicating good feeling about the process and service
expressing or meaning a refusal or denial.
Detecting sadness, hate, violence and inadequacy about a new product
Showing emptiness and usefulness about the service or process
Drastic changes need to be made
Neither good nor bad, neutral comments aren't helping your reputation, but they aren't hurting it either.
Sign of concern since the customer can quickly turn to the positive or negative
Not containing any definitive sentiment
How Does Sentiment Analysis Work?
The sentiment analysis algorithm determines if a chunk of text is positive, negative or neutral. It uses natural language processing (NLP) techniques such as part-of-speech tagging, lemmatization, prior polarity, negations, and semantic clustering.
Part-Of-Speech tagging (POS tagging)
Part-of-speech (POS) tagging is a popular Natural Language Processing process which refers to categorizing words in a text (corpus) in correspondence with a particular part of speech, depending on the definition of the word and its context.Learn More
In lemmatization, we try to reduce a given word to its root word. The root word is called a stem in the stemming process, and it is called a lemma in the lemmatization process.
A lemmatization algorithm would know that the word better is derived from the word good, and hence, the lemme is good. Because lemmatization involves deriving the meaning of a word from something like a dictionary, it’s very time consuming. So most lemmatization algorithms are slower compared to their stemming counterparts. There is also a computation overhead for lemmatization, however, in an ML problem, computational resources are rarely a cause of concern.Test for Free