This dataset "Kalki 2898 AD" contains comments ✍️ and ratings ⭐ of various viewers who enjoyed the movie in recent times. 🎥
The aim of this dataset is to perform sentimental analysis with the help of Natural Language Processing libraries and Machine Learning algorithm - Logistic Regression.
NOTE- Based on the ratings, we can pre- classify that the ratings above 7 stars is considered as positive, below 5 stars as negative and the remaining as neutral comments.
You can find the dataset through my kaggle account - https://www.kaggle.com/datasets/sudarsan27/sentimental-analysis-movie-review
The aim of this dataset is to purely understand the concepts of sentimental analysis.
Sentiment analysis is a form of text research that uses a mix of statistics, natural language processing (NLP), and machine learning to identify and extract subjective information — for instance, a reviewer’s feelings, thoughts, judgments, or assessments about a particular topic, event, or a company and its activities.
You can learn more about reputation management in general from our separate post.
This analysis type is also known as opinion mining (with a focus on extraction) or affective rating. Some specialists prefer the terms sentiment classification and extraction. Regardless of the name, the goal is the same: to know a user or audience opinion on a target object by analyzing a vast amount of text from various sources.
Basically, sentiment analysis distinguishes three types of emotions — negative, neutral, and positive. It can be applied to a separate sentence or its part as well as being used for document classification, where the term document covers a broad range of textual items like emails, reviews, comments, articles, and more.
Sentiment analysis comes in many forms — depending on the tasks and objectives you pursue. But in practice, several types are often combined to solve complex real-life problems.
Subjectivity classification Subjectivity classification divides fragments of text into objective and subjective or opinionated. An objective sentence contains facts and neutral information: Three strangers are reunited by astonishing coincidence after being born identical triplets, separated at birth, and adopted by three different families. In turn, a subjective sentence expresses someone’s attitude, feelings, judgment, belief, and more: This apartment is wonderful. I enjoy every minute I spend in here.
Since subjectivity classification filters out neutral statements, it often serves as the first step of polarity classification.
Polarity classification Opinionated pieces of text can be further divided into negative and positive, using polarity classification. This technique works for large-scale studies of positive and negative trends in text data like product reviews, social media posts, or customer feedback.
Advanced models go further than just binary classification, identifying the sentiment intensity. In this case, text pieces are categorized into more than two groups — for example, extremely positive, positive, neutral, negative, and extremely negative. The multiclass approach allows not only for solving subjectivity and polarity tasks simultaneously. It also provides more precise results when processing sentences with comparative expressions (better, most disgusting, etc.) and modifiers (too, so, completely, and so on.)
Yet, even multiclass polarity classification has a lot of limitations. It doesn’t detect the customer’s attitude to different aspects or characteristics of your services — which is critical for making improvements and successful product development. That’s where an aspect-based sentiment analysis may help you out.
Sentiment analysis allows you to look at your operations from a customer point of view. But how do you extract that knowledge from user-generated data? These are common steps to create a custom opinion-mining model by the forces of an in-house or external data science team.
Data collection. First, you need to gather relevant brand reviews and mentions in one dataset. You can collect feedback from your own website or partner with resources that own such data.
Read our article on data collection for machine learning to dive deeper into the topic.
Sentiment annotation. The second step is to assign sentiment tags (positive, neutral, negative, etc.) to words and phrases. Attribute-based and fine-grained types of sentiment analysis will require more labels — and more textual data — to produce accurate results. Keep in mind that sentiment labeling is considered reliable if it’s made by more than one annotator.
Text cleansing. Reviews and comments typically contain a lot of irrelevant and excessive information that can negatively affect a model's precision. So, before feeding the dataset to an algorithm, you must get rid of noises, stop words (articles, pronouns, etc.), and variations of the same words, transforming them into canonical form.
To learn more, read our article on preparing your dataset for machine learning or watch our dedicated video explainer.
Note that all the above-mentioned steps are conducted by freelancers or trainees rather than by experienced data scientists. Moreover, to save time and money, you can take advantage of public datasets for machine learning annotated for sentiment analysis tasks. Some examples are Trip Advisor Hotel Reviews, Sentiment140, and Stanford Sentiment Treebank.