The process of analyzing a text and identifying the opinion in the text those are subjective and classifying them as positive, Negative or Neutral is known as
To understand it in detail let’s take an example where you tend to receive a lot of text. This is in the form of online reviews of your products, NPS responses, or conversations on Twitter. All of these texts are important to your business and your brand reputation as these forms the important data you’re your business. It is always a curious thing to know the overall sentiment expressed by customers in each piece of text. For a small content piece, it is a easy task. However, if you the data that is huge then reading and making sense of it will take hours or even days.
Sentiment analysis is a set of Natural Language Processing (NLP) techniques that extracts the opinions mentioned in the given text by taking a text written in natural
language. This might be in form of academic circles or a document.
The objective of Sentiment Analysis may be understood as a process to take a text and produce a label (or labels) that describes briefly the sentiment of that text, e.g. positive, neutral, and negative. Let’s say for instance we are looking at hotel reviews and the sentence ‘The support from the hotel staff was of first class” would be labeled as Positive and the sentence ‘The shared bathroom provided was not comfortable and disgusting’ to be labeled as Negative.
IF you are asking a machine to do this for you, then it is definitely not an easy task. The skills required for this would be knowledge of different fields such as Statistics, Computer Science and Linguistics.
Sentiment Analysis in a nutshell a boon to business as it helps them with easy and quick processing and extraction of actionable insights from large text volumes
without reading it. To be precise, this technique is useful in understanding the user behavior about something measureable. This can help businesses in
understanding customer behavior on social media platforms,, product reviews, or NPS comments. Sentiment Analysis is a method to enhance organization’s
understanding of customer opinions and actions.
Sentiment Analysis is an automated process that allows you to perform analysis of texts in real-time and always against the same set of criteria. You aren’t dealing with several people with different biases at work, but rather with a single unified system that has a consistent output.
This can begin from the scratch by introducing your own application or using any of the well recognized open-source libraries available such as Scikit-learn.
This looks easy however might be a tedious task to implement it. Machine Learning is not easy and it takes efforts of resources to build and a bunch of expert data scientists. Then there would be a need of collection of data chunk of upmost quality which will be used to train the models, source some hardware (including GPUs) for running the software on and test it continuously to get a solution that works.. Then, when it’s built and is working then more resources are required to integrate the new module into your existing solution, to maintain it, and to keep it updated.
Creating Your Own Sentiment Analysis Model
Important thing to remember in Machine Learning is that a model will perform well on texts that are similar to the texts that are used to train it.
In case the texts differ in this model then it will not be compliant and effective that means If you have trained your sentiment analysis model using survey responses then it will work If the texts differ then this model will not be effective, meaning if you have trained your sentiment anlayis model by using survey responses then it will work perfectly for any or all new survey responses. However, it will not give a good response to other variations such as tweets.
Generic sentiment analysis models are pretty good for many use cases and getting started right away, but sometimes it’s not enough – you need a custom model trained with your own data. We put a lot of love into creating our models, and they were trained with a lot of data, but their performance can be improved upon for smaller and more specific problems.
Another reason why you might want to train your own custom model is the labeling criteria. Consistency is considered as one of the main requisites of automatic classification but if the original criteria used for labeling is not useful for your case, then the model will not work for you. In other words, what is negative for one organization may be a positive one for you.
Data for Training the Model
There is a saying that goes, garbage in is the garbage out holds true for the training data of machine learning. Without a quality data, the model is never considered a good one. For this example, you can use this dataset, composed of texts from hotel reviews. The dataset is a CSV file with two columns: Text and Sentiment, which can be one for negative or positive.
Not all the texts of the dataset are tagged. API will train a model with the tagged texts, and then you can keep improving the model by tagging more texts yourself using our UI.
Training the Sentiment Analysis Model
Creating a custom model is simple. All you need to do is, upload your data and tag it if needed, and the model will learn from this data. API automatically chooses the best parameters and handles the training for you.