Sentiment analysis is considered one of the most popular strategies businesses use to identify clients’ sentiments about their products or service. But what is sentiment analysis?
For starters, sentiment analysis, otherwise known as opinion mining, is the technique of scanning words spoken or written by a person to analyze what emotions or sentiments they’re trying to express. The data gathered from the analysis can help businesses have a better overview and understanding of their customers’ opinions, whether they’re positive, negative, or neutral.
You may use sentiment analysis to scan and analyze direct communications from emails, phone calls, chatbots, verbal conversations, and other communication channels. You can also use this to analyze written comments made by your customers on your blog posts, news articles, social media, online forums, and other online review sites.
Businesses in the customer-facing industry (e.g., telecom, retail, finance) are the ones who heavily use sentiment analysis. With a sentiment analysis application, one can quickly analyze the general feedback of the product and see if the customers are satisfied or not.
How does Sentiment Analysis Work?
To perform sentiment analysis, you must use artificial intelligence or machine learning, such as Python, to run natural language processing algorithms, analyze the text, and evaluate the emotional content of the said textual data. Python is a general-purpose computer programming language typically used for conducting data analysis, such as sentiment analysis. Python is also gaining popularity as it utilizes coding segments for analysis, which many people consider fast and easy to learn.
Because, nowadays, many businesses extract their customers’ reviews from social media or online review sites, most of the textual data they’ll get is unstructured. So, to gain insight from the data’s sentiments, you’ll need to use a natural language toolkit (NLTK) in Python to process and hopefully make sense of the textual information you’ve gathered.
How to Perform Sentiment Analysis in Python
This blog post will show you a quick rundown on performing sentiment analysis with Python through a short step-by-step guide.
Install NLTK and Download Sample Data
First, install and download the NLTK package in Python, along with the sample data you’ll use to test and train your model. Then, import the module and the sample data from the NLTK package. You can also use your own dataset from any online data for sentiment analysis training. After you’ve installed the NLTK package and the sample data, you can begin analyzing the data.
Tokenize The Data
As the sample text, in its original form, cannot be processed by the machine, you need to tokenize the data first to make it easier for the machine to analyze and understand. For starters, tokenizing data (tokenization) means breaking the strings (or the large bodies of text) into smaller parts, lines, hashtags, words, or individualized characters. The small parts are called tokens.
To begin tokenizing the data in NLTK, use the nlp_test.py to import your sample data. Then, create separate variables for each token. After tokenizing the data, NLTK will provide a default tokenizer using the .tokenized() method.
Normalize The Data
Words can be written in various forms. For example, the word ‘sleep’ can be written as sleeping, sleeps, or slept. Before analyzing the textual data, you must normalize the text first and convert it to its original form. In this case, if the word is sleeping, sleeps, or slept, you must convert it first into the word ‘sleep.’ Without normalization, the unconverted words might be treated as different words, eventually causing misinterpretation during sentiment analysis.
Eliminate The Noise From The Data
Some of you may wonder about what is considered noise in textual data. This refers to words or any part of the text that doesn’t add any meaning to the whole text. For instance, some words considered as noise are ‘is’, ‘a’, and ‘the.’ They’re considered irrelevant when analyzing the data.
You can use the regular expressions in Python to find and remove noise:
- Punctuation marks
- Special characters
You can add the code remove_noise() function to your nlp_test.py to eliminate the noise from the data. Overall, removing noise from your data is crucial to make sentiment analysis more effective and accurate.
Determine The Word Density
To determine the word density, you’ll need to analyze how the words are frequently used. To do this, add the function get_all_words to your nlp_test.py file.
This code will compile all the words from your sample text. Next, to determine which words are commonly used, you can use the FreqDist class of NLTK with the code .most_common(). This will extract a date with a list of words commonly used in the text. You’ll then prepare and use this data for the sentiment analysis.
Use Data For Sentiment Analysis
Now that your data is tokenized, normalized, and free from noise, you can use it for sentiment analysis. First, convert the tokens into a dictionary form. Then, split your data into two sets. The first set will be used for building the model, and the second one will test the model’s performance. By default, the data that will appear after splitting it will contain all the listed positive and negative data in sequence. To prevent bias, add the code .shuffle() to arrange the data randomly.
Build and Test Your Sentiment Analysis Model
Lastly, use the NaiveBayesClassifier class to create your analysis model. Use the code .train() for the training and the .accuracy() for testing the data. At this point, you’ll retrieve informative data listing down the words along with their sentiment. For example, words like ‘glad,’ ‘thanks,’ or ‘welcome’ will be associated with positive sentiments, while words like ‘sad’ and ‘bad’ are analyzed as negative sentiments.
The Bottom Line
The point of this quick guide is to only introduce you to the basic steps of performing sentiment analysis in Python. So, use this brief tutorial to help you analyze textual data from your business’ online reviews or comments through sentiment analysis.