Which Model is Best for Sentiment Analysis

There is usually a plethora of ways to manage a certain process. However, efficient and time-saving techniques always go along to make that process more productive. Sentiment analysis or opinion mining is the ability of a system to comprehend human emotions from a given text. Sentiment analysis is no different, as it has many techniques and models to look to when making insightful analyses.

The best sentiment analysis models use machine learning & artificial intelligence to assess vast swathes of data. These models rely on humans training these computers to attain the maximum accuracy in their analysis. Finding the best Model for performing sentiment analysis won’t be easy unless you know the strengths and weaknesses of each Model.

Why Is Sentiment Analysis Important?

Before we delve deeper into the technicalities of natural language processing, neural networks, deep learning algorithms and sentiment lexicons, to name a few, we must assess the utility sentiment analysis provides to companies and organizations. Sentiment analysis has gained plenty of customer support since its inception.

Customer reviews and feedback have always been critical in product development. An organization can learn from its mistakes or weaknesses in its products. What better way to pay heed to what the customers are saying? Organizations can tread on the path of continuous improvement once they teach the customer sentiments in their product development phase.

How Sentiment Analysis Models Work

Sentiment analysis is done through natural language processing, where textual input is changed to computer-understandable code. A computer can give a sentiment score to a similar input text. The first step in this process is generating sentiment libraries that store all the different sentiment lexicons and grammatical rules to evaluate the text fed to the program.

Formulating a sentiment library needs a training dataset. We can use standard datasets for this purpose or make our very own custom dataset which would require more hours to be put in. Once we have our training dataset, we teach the computer how to distinguish between positive, negative, and neutral sentiments.

The process is similar to human cognizance of new phrases or language. A computer is taught the syntax and lexical components of paragraphs and phrases. During this training time, humans assign codes to different phrases and words. Once the computer applies the rules, it will assign a sentiment score to a phrase or sentence.

3 Algorithms Models You Consider for Sentiment Analysis

Once you have prepared the sentiment library, the next step involves selecting an algorithm model to evaluate the data. Choosing the best sentiment analysis model for your business or organization can prove tricky if you don’t know how each Model operates. The three most common models for sentiment analysis are:

Rule-Based Algorithm Model

A simple sentiment analysis model extracts sentiments based on the rules fed to the evaluator. These rules distinguish between negative and positive connotations and assign a score to each occurrence. Denoting the sentiment to be positive or negative depends on the occurrence of positive & negative features of the phrase. Rule-based algorithms are swift, yet their analysis can be misleading as they fail to cater to the overall context of a text by focusing on occurrences of specific sentiments.

Machine Learning Models

Machine learning sentiment analysis models rely on the ability of machines to pick up emotions in each context. Instead of looking for specific words that depict negative or positive sentiments, the computer is taught how to distinguish different emotions. Humans feed sample passages to the algorithm to learn different emotions.

  1. Traditional Model where the algorithm is given examples of positive & negative sentiments in each dataset. SVM or Support Vector Machines, a traditional model, is scalable and can be used on large datasets to interpret the polarity of textual input.
  2. Deep Learning Algorithms are the ones that use neural networks to imitate a human brain to analyze sentiments. Deep learning algorithms are known to exhibit greater accuracy than their traditional counterparts.

Hybrid Models

In the pursuit of accuracy, you can rely on hybrid models as they combine the strengths of machine learning and rule-based techniques. The power of deep learning and artificial intelligence is morphed into a customized setup that helps detect human emotions and sentiments.

More Advanced Models

Many sentiment analysis models help you extract emotions from textual input. Complex sentiment analysis models rely on ever-evolving technologies to offer you cutting-edge results and accuracy. These deep learning models are self-reliant in that they can learn from their work and carry on a path of continuous improvement to increase accuracy. Some of these advanced models rely on neural networks to imitate the human brain’s capability to resolve problems.

Choosing the Right Model

Knowing about the technical jargon we have just shared with you is a precursor to selecting the most optimum sentiment analysis model. To recap, we shall go over the process one more time. The process starts with data mining or gathering data, which is then compiled into datasets. The dataset is then classified into different categories to break down the text into understandable code for the program.

Classification algorithms are critical when training the sentiment analysis model. Training the algorithm is the key to unlocking the true potential of any deep learning model. Datasets are also a key component from which information is extracted by these algorithm models. Naïve Bayes, linear regression support vector machines, & deep learning are some of the classification algorithms used for sentiment analysis.

After the classification, rule-based or deep-learning models are used to analyze datasets. This analysis can help extract useful information about your business, its strengths & weaknesses, points of improvement and so on, ultimately leading to a better and more efficient business.

VizRefra offers cutting-edge sentiment analysis based on the latest deep learning models to analyze your data for actionable insights. Our team, with its expertise in the latest deep learning and AI-Based sentiment analysis models, is here to help you in every endeavour. A result is a holistic approach towards your business and a more efficient way of conducting it.