What is the Fundamental Purpose of Sentiment Analysis on Social Media?
The sentiment analysis mechanism can decipher the feelings hidden within the human language. It processes every word to derive the meaning and emotion in them. One can analyze comments, feedback, and anything else. You can use TextBlob sentiment analysis or any other opinion mining technique to achieve your goal. These days, understanding the client’s perspective is necessary. If a business isn’t aware of people’s feelings, it cannot fulfill its goals. Thus, opinion mining serves multiple purposes.
Various libraries and packages help us perform opinion mining. A powerful way to perform opinion mining is by using TextBlob. It helps us achieve our purpose most efficiently. The following paragraphs tell about TextBlob and how it comes in handy while performing emotion analysis.
TextBlob and Opinion Mining
Using TextBlob analysis, we can interpret customer feedback and reviews. You can use it on data extracted from social media or other places. It accurately derives the emotion and the intensity behind words. TextBlob is a library in python that helps process natural language. Machines do not understand human language or emotions. Humans feel sorrow, happiness, and other emotions, but a computer cannot decipher these sentiments. Thus, we need a Natural Language Processing mechanism. That is where the python library TextBlob comes in use.
It uses NLTK actively to decode hidden emotions within words. NLTK (Natural Language Toolkit) gives access to helpful resources. Users can categorize and classify several tasks. When you use TextBlob, you can easily use complex analysis methods on any data type. One can even perform an advanced operation on textual data.
How Does TextBlob Sentiment Analysis Work?
To work with TextBlob, you must install relevant libraries before anything else. Start by using the pip install command to get TextBlob. With the right library, you can analyze any dataset. There are negative and positive words in pre-installed libraries. Using available data, our system deduces emotions behind new words.
However, before going ahead with classification, our program will perform numerous steps:
- Tokenization: It is the process of converting sentences into words, called tokens.
- Translation: The system will also transform and translate languages to extract the exact meaning behind words. For example, if something is in French, it will be translated into a known language.
- Lemmatization: We convert all sentences to their root words for efficient analysis.
- Finding word count: The program finds the total word count. Usually, TextBlob provides easy-to-use n-grams.
TextBlob makes this entire process extremely easy. Each word gets a score. If the word is negative, it gets a negative score. For positive sentiments, the score is more than zero. After all, words get a score, the total is used to find an average value. It determines whether a text piece is positive or negative. The system is advanced enough to deduce the meaning behind emoticons, punctuation, and emojis.
Role of Natural Language Processing in TextBlob Opinion Mining
Modern NLP tools help perform accurate analysis in real time. It reduces your workload and the need for writing lengthy codes. You can provide the system with an article, personal opinions, or subjective sentences, and it will dig out negative sentiments in minutes. NLP has several practical applications. Whether you need to parse a sentence or perform analysis, it performs both tasks without delays.
Significance of Sentiment Score
A sentiment score shows the intensity of emotion in a phrase. Our analysis mechanism assigns individual scores to every sentence depending on their positive or negative nature. Once the system has all the scores, they are used to find an average score. It will show the overall emotion in the text. Without this score, your system cannot perform its task.
Polarity and Subjectivity in TextBlob Opinion Mining
When it comes to TextBlob emotion analysis, the results are in the form of polarity and subjectivity. Polarity can be negative or positive and ranges from -1 to 1. In this system, a positive score defines positive polarity, and a negative value indicates negative polarity. Text having negations will see a shift in overall polarity. Sometimes, a score can be zero when a piece of text is neutral.
Similarly, you also find subjectivity value within the results. Subjectivity can be your personal feelings, beliefs, desires, or suspicions. It quantifies the opinions given in textual data between 0 and 1. If a sentence has strong opinions, the subjectivity will be higher. For an accurate estimate of subjectivity, each word is analyzed.
Why is TextBlob Emotion Analysis Significant?
Reviews have become a part of every business operation. There is hardly any brand that doesn’t ask for customer opinion. The reason for taking feedback is to understand how someone perceives a brand and its product. If your company sales are down, you must find the underlying reason. The best way is through feedback. However, analyzing thousands or millions of comments is not humanly possible. So, TextBlob sentiment analysis can help you find negative reviews and emotions. If negative emotions exceed positive ones, you need a new business strategy.
With such analysis mechanisms, your life becomes easier because you can find hundreds of emotions within minutes.
Advantages of Using TextBlob
TextBlob is more effective for opinion mining because it is simple and performs multiple functions. You can tokenize words, translate paragraphs and detect intense emotions. Moreover, it’s easy to analyze hundred or millions of records. Instead of just using NLTK, it is better to use TextBlob. Analysis with TextBlob is faster than with NLTK.
Practical Uses of TextBlob Sentiment Analysis
Text analysis mechanisms are used widely in every industry. You will see every business carrying on their everyday tasks with the help of such effective systems. When it comes to TextBlob opinion mining, you will see its frequent use in the:
Social Media Platforms
From Facebook to Instagram, there are multiple platforms where you post about your products. In return, customers write comments. After collecting and analyzing these comments, you get an idea about the client’s perception of a product or service. It helps your business understand the audience’s mindset, and you can get meaningful insights.
Movie Review Systems
Movie review systems are another place where this kind of text analysis system comes in handy. You can analyze reviews to see which film is more liked by an audience and why it is a huge success. The same goes for movies suffering losses.
Another popular application of TextBlob sentiment analysis is in the news industry. You can analyze thousands of reviews through these systems and understand public emotions.
Getting Familiar With TextBlob Analysis System
As told earlier, TextBlob is a python library that helps us process human language and derive the emotional meaning of words. It is an efficient tool that works with all kinds of data and provides us the flexibility to analyze massive amounts of data within a short period. One can integrate TextBlob with other algorithms to use it for news, movie reviews, or social media analysis. It also interprets the sentence polarity. So we understand a person’s feelings about a product or service. Since the system is easy to call and serves multiple purposes, it is becoming a popular emotion analysis option.
Opting for the Right Help
Decoding sentiments is not a trouble anymore. Advanced sentiment analysis systems make the processing of human language easier. However, for a successful business, you have to look at multiple factors. It involves preparing strategies, business models, and much more. The task might seem intimidating. Thus, VizRefra provides guidance and helps to build a successful business. We provide several services, including text sentiment analysis services, financing strategies, and experts to assist you in higher-level execution. Contact us today through our website.