What is Sentiment Analysis? (Beginners Guide)

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 Artificial Intelligence   uses Natural Language Processing (NLP) as a method to communicate with intelligent systems using natural language. NLP develops computers to perform tasks with the natural languages humans use. Input and Output of NLP can be #Speech  #Written Text.



Sentiment Analysis


Sentiment Analysis is one of the most popular NLP tasks. Machine learning models are trained to classify text by polarity of opinion(positive, negative, neutral, and everywhere in between).

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Sentiment Analysis is a method of detecting positive & Negative sentiment in text(eg: Analyzing 5000+ customer reviews). It focus on polarity(positive, negative, neutral) but also on feelings(sad, angry, happy), intentions, urgency.

Types of Sentiment Analysis

EMOTION DETECTION:

This type of sentiment analysis aims to detect emotions like happiness, sadness, anger and so on.Many emotion detection systems use Lexicons(List of words and the emotions they convey) or other complex computer algorithms. Downsides of Lexicons is that humans express emotion in different ways(eg: "your customer support is killing me". might also express eg: "congrats you are killing it") when we use keyword like "kill".

ASPECT-BASED SENTIMENT ANALYSIS:

"The product was great but the service was poor". In this statement we have more than one statement and more than one sentiment. Now here we use Aspect-Based Sentiment Analysis. It extracts and separates each aspects and sentiment polarity in the sentence.

"The product was great"  - Product-Positive 

"Service was poor" - Service-Negative

MULTILINGUAL SENTIMENT ANALYSIS:

Contextual references, Cultural subtilities, and colloquialisms expressed by customers or in reviews can be very hard to pick my machine translation. Multilingual Sentiment Analysis identifies and analyze customer reviews, emotions, surveys across the social media. It simplify analyzing the Voice of the Customer(VoC).

LEXICONS

Lexicons serve as dictionaries for extracting sentiment from raw unlabeled data. These are useful in estimating semantic orientation(polarity). They are applied to polarity prediction tasks and serve as a Bag of Words(BoW) that help assign a score/label to terms in text.

EMOTIONAL LEXICONS 

A list of emotions and words that express each emotion is called " Emotional Lexicons". It's step one is detecting the keywords or phrases that associate with emotions and it can be achieved by using parts of speech (Noun, Adjective, Adverb etc.)

Applications of SENTIMENT ANALYSIS

  • We can monitor brand sentiment online in real time.
  • Quickly detect negative tweets that mention your brand or any specific product.
  • Understand what customer needs, and make data based decision on the go.

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