Custom Sentiment Analysis - What, Why & How
Sentiment analysis is a growing field today, thanks to the availability of feedback channels and communication channels. The best businesses are the ones that understand their customers - their thoughts, emotions, and feelings.
What is Sentiment Analysis?
Sentiment analysis is the process of analyzing the sentiment behind a piece. It can be positive, negative, or neutral. Also known as Opinion mining or Emotion AI, it uses NLP, text analysis, computational linguistics, and biometrics to extract and study the affect and the subjective information. NLP sentiment analysis enters into the emotional spheres assisting companies to enhance their user experience.
Since brand reputation and loyalty have gained importance, companies must know what their customers and others think. Free online sentiment analysis tools assist them. Companies use sentiment analysis for analyzing the following - reviews, tweets, comments, brand community communication channels, etc.
Types of Sentiment Analysis
Following are four different types of sentiment analysis
1. Objective or subjective classification -
Objectivity and subjectivity are two sides of the same coin. An Objective text speaks to the facts, whereas a subjective text reflects emotions. This type of analysis identifies whether the text is subjective or objective.
2. Fine-grained analysis
Fine-grained sentiment analysis studies polarity. Polarity is an essential aspect for several businesses. It allows them to determine the intensity as well as provides them with enough data in hand to make comparisons.
It is often used for 5-star ratings. For companies to rely on fine-grained sentiment analysis, they can make their feedback ratings more extensive by spreading it out in the following way - Strongly Negative, Weakly Negative, Neutral, Weakly Positive, and Strongly Positive.
3. Lexicon-based analysis
Also known as Emotion analysis, lexicon-based analysis detects emotion. Lexicons are a list of words and the feelings they convey. It is one of the things used by emotion detection systems. They may also use more complex algorithms.
A backdrop of using this analysis is the subjectively unique use of lexicons today. For example, the word ‘killing’ can be both positive or negative depending on the context.
4. Feature-based analysis
Also known as Aspect-based sentiment analysis and Individual target sentiment analysis, Feature-based sentiment analysis analyzes specific features of an entity. For example - phones can be analyzed depending on their battery life, screen, RAM, processor, etc.
It assists in figuring out if the individual features of an entity or product are positive, negative, or neutral.
Boons of sentiment analysis
Simplifies large scale data
A business receives hoards of data every day - thousands of tweets, product reviews, comments, etc. Having to analyze all this data to make use of it is a burdening task prone to errors. Sentiment analysis helps businesses to simplify this data into clearly defined parts, highlighting the strengths and weaknesses. It further helps strategize business development and marketing plans.
Monitoring brand performance
Brands that evoke certain feelings, thoughts, and emotions in their users are in the process of building and upholding brand loyalty. Apple, for example, is associated with innovation and luxury. In contrast, Tissot is associated with affordability. Consumers often share their views of your brand on social media channels. Sentiment analysis will help you monitor the sentiments (positive or negative) towards your brand. You can use this analysis to shape your products, campaigns and enhance user satisfaction.
Politics
Turning the audience votes in your favour or swaying their support has become extremely easy. All thanks to social media and other communication channels. Sentiment analysis helps you understand how the public feels towards a political figure, what irks them, what they appreciate etc. It can be used for or against a political figure or a party.
How does it work?
Sentiment analysis combines NLP and machine learning algorithms to analyze the text. There are two major approaches to sentiment analysis - Rule-based and machine learning techniques.
Rule-based Method
The rule-based approach works on pre-defined rules. Crystal clear definitions are used for identification. It makes use of several NLP techniques like Stemming, Tokenization, Part-of-speech tagging, and Parsing. It also makes use of Lexicons. The rule-based approach works in the following way -
- Two defined lists of polarized words
- The total number of positive and negative words in a text.
- If the number of positive words is greater than the negative, it will result in positive sentiment. If opposite, it will result in negative sentiment.
The rule-based approach requires fine-tuning and hence, requires more maintenance. It fails to understand how different words can be grouped together to explain emotion in a context. Moreover, it results in a lack of precision.
Machine learning techniques
In contrast to the rule-based approach, the machine learning techniques to sentiment analysis does not rely on handcrafted rules. It uses several classification algorithms like Naïve Bayes, Logistic Regression, Support Vector Machines, or Neural Networks. The use of machine learning techniques enables it to dig deeper, making it more reliable and precise.
Conclusion
Sentiment analysis is a powerful tool that can enhance your business and foster growth. Brands around the world are leveraging this tool to stay on the top of their game. Textrics offers text analysis to help your business propel and grow in leaps and bounds with just a click. We will assist your business to become more consumer-centric and improve your customer experiences.