How to Conduct Customer Sentiment Analysis in Python

Every business relies on customers to make purchases and come back to them time and time again. To ensure customers are happy, you must be able to analyze their thoughts and feelings about your business and products. Customer sentiment analysis enables you to do that.

By learning how to implement customer sentiment analysis in Python, you can create your own robust strategy for determining sentiment polarity – whether they are positive, negative, or neutral.

Recognizing the value of your customer sentiment score enables your business to make better decisions that influence your company’s future. You know what to do better, what to provide, and what strategies may be out of line with your growth potential. Utilizing this tutorial, you will learn how to complete customer sentiment analysis in Python, including how it works and the process you’ll use.

What Are Customer Sentiment Analytics?

Conducting customer sentiment analysis in Python allows you to use natural language processing (NLP) and machine learning to analyze customer feedback. It enables you to determine how various specific topics impact your customers, such as to determine if their feedback is positive, negative, or neutral.

Let’s provide an overview of why this is important and then dive into how to create customer sentiment analysis with Python.

Why does customer sentiment matter? Customer sentiment provides direction to a business. It is the process of capturing information from various sources or analyzing a specific group of customers. Utilizing this information, your business learns:

• What the customer is experiencing with your product or service
• If the customer is loyal or looking for a replacement service
• What other services or features may be best to better support your customer over time

Learning how to do this enables you to have a constant focus on what your customers’ feel and are experiencing, enabling you to make real-time decisions sooner and, often, mitigate high-risk situations fast.

By collecting data from various resources and analyzing it, you could compete better, achieve more, avoid costly mistakes, and create a strong brand. If you’re reading to see what customer sentiment analysis means and how it can work for your objectives, read on.

Business use cases for customer sentiment analysis include:

• Pinpoint opportunities: Locate areas of opportunity for your business to improve and grow, such as concerns that customers have.
• Brand insights and crisis management: If a product or service does not perform as expected, customer sentiment enables you to learn what people are saying and how they feel.
• Competitive analysis: You can use these tools to help you assess how well your competitors are doing, finding potential gaps you can take advantage of within your business.
• Market research: Learn more about your market and complete a trend analysis to better understand reactions to trends, public events, and other data points.
• Campaign performance: Utilize these tools to help you monitor how well any campaign is doing for your business.

How It Works: Customer Sentiment Analysis Meaning and Application

Gathering data from various locations- such as social media and reviews – can help you gain some valuable insight into what your customers think. With the method we describe, you gain a far more comprehensive level of insight.

To do that, you need to understand what a customer sentiment score is. This is a quantitative metric that will measure the emotions and opinions of your customer towards your product, service, or brand.

The score itself ranges from 0 to 100. To calculate it, the percentage of negative responses (called detractors) is subtracted from the percentage of positive responses (called promoters). A good score is dependent on a variety of factors, including the industry you are in. You can use this guide from Retently to offer some insight into where you stand based on other businesses within your sector. For example, in retail businesses, the average sentiment score is 64, while in construction, it is 37.

The actual calculations are complex, and time consuming to try to determine yourself. However, with customer sentiment analysis with Python, you will be able to see this information quickly and gain the information that you need.

How to Implement Customer Sentiment Analytics with Python

In this section of the tutorial, we will break down the entire process, including the steps to take to build your own tool.

Data collection. The first step in the process is to collect data. This data is what will be analyzed to provide an accurate customer sentiment score. Ultimately, you can collect data from any location that is important to you, as a business. However, most companies will want to focus on the readily available insights they have, such as social media, reviews, survey information, and through the use of tools likes like Aps that can help to capture specific mentions.

Web scraping is the process that makes this happen. Web scraping allows you to create a piece of software code that allows you to capture specific information from the web. For example, you could use it as a way to capture reviews for your company. To ensure efficiency, consider the importance of automating web scraping. When you do, you will save yourself time and be able to capture more of the information you need without human intervention (and that saves you money, too).

A variety of web scraping tools exist, including two that we have talked about significantly at Rayobyte: BeautifulSoup web scraping and Scrapy web scraping. You can also use a web scraping API that will handle the entire task for you. An API gets you up and running faster in this process.

No matter which tool you use, we also recommend utilizing a proxy service to help you protect your IP address and other sensitive information online.

Text Processing. Once you have the data, you need to do something with it. How can we understand raw data from thousands and even hundreds of thousands of data points? You need to put that text through preprocessing. During this process, we are looking at:

• Tokenization: The process of replacing sensitive data with data that’s not sensitive, called a token. Tokenization does not change the value, but it protects sensitive information.
• Stop Word Removal: Stop words are those less informative words in a sentence like “the” and “a”. This process removes these words to improve efficiency and accuracy in the analyzation of text later.
• Lemmatization: In Python, lemmatization refers to the process of reducing words to their base or dictionary form by considering the word’s context and meaning. We encourage you to use NLTK or SpaCy to do this.

Sentiment Classification. The next step is to apply sentiment classification, which means determining if the feedback is neutral, positive, or negative. There are numerous pre-built tools that can help you with this process. These tend to be the ideal route to take for any type of customer sentiment analysis Python project because they can save you a significant amount of time. Consider the following:

• VADER: VADER is ideal for short social media texts. It operates by analyzing the polarity of words and then assigns a sentiment score to each word. It uses emotional value to make these decisions.
• TextBlob: For those who want a rule-based analysis methodology, TextBlob is the ideal choice. This Python library uses a simple API for tasks like parts-of-speech tagging, noun phrase extraction, classification, and as noted sentiment analysis.
• BERT: Another potential route is to use a transformer-based model, like BERT. This tool is best for deep learning-based sentiment detection.

There are numerous sentiment analysis models available. You may have more than one that you consider your favorite. Or, you may want to try a few different options. No matter which one you select, integrating them with Python is a direct and straightforward process.

The code to implement that is also rather simplistic (update as necessary):

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pip install -q transformers
from transformers import pipeline
sentiment_pipeline = pipeline("sentiment-analysis")
data = ["I love you", "I hate you"]
sentiment_pipeline(data)

In this method, we are using the pipeline class to make predictions from the models. This process relies on a default model for sentiment analysis to analyze the list of text data. IT will then provide you with outputs such as:

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[{'label': 'POSITIVE', 'score': 0.9998},

 {'label': 'NEGATIVE', 'score': 0.9991}

Here is an example of the Python code you might use if you want a sentiment analysis model that is specific to your language. You can also choose them based on use case by simply providing the name of the model within the text. For those who want to analyze tweets, for example, to capture brand mentions, you might want to use:

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specific_model = pipeline(model="finiteautomata/bertweet-base-sentiment-analysis")

specific_model(data)

Visualize Your Data. The final step in this process is to visualize the outcome. There are also various tools to help you to do this, but there are two that we recommend using overall. These are beset when it comes to tracking sentiment trends over a period of time.

• Matplotlib: This is a detailed Python library that is ideal for creating interactive visualizations (as well as animations and static charts). It allows you to take the data you have an actually see what it means – rather than just texts of basic information. Check out Matplotlib to see how well it can work for your needs. With versatility to create publication quality plots, this is an excellent tool for advanced and beginners to use.
• Seaborn: Another Python library for visualization based on Matplotlib. It is a higher level interface and tends to provide a more elevated final product. If you are creating customer sentiment analysis in Python to help you with a demonstration or presentation, Seaborn will help you to take a more in-depth and professional view on your data.

The Code to Get You Started with Customer Sentiment Analytics in Python

It’s beneficial to work through each of the steps above to create a comprehensive plan that fits your needs. To help you, we have created a basic level of preparation you can use for customer sentiment analysis in Python.

Get the libraries you need first. Overall, we recommend the following (and you will need to import any you do not have).

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from google.colab import drive
from collections import Counter
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import re
import nltk
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
from transformers import pipeline
from wordcloud import WordCloud
from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer

Once you take that step, the next step will be to clean up that data. For this project, we’ll focus on capturing a set of reviews on a retail location. To do that, we will want to capture specific information such as:

name, overall_rating, rating, trip_type, date_of_review, original_review_text, and translated_review_text.

The next component of this process is to work through tokenization, removal of stopwords, and lemmatization. Here’s an example of what the code might look like for this:

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# Initialize lemmatizer and stopwords list
lemmatizer = WordNetLemmatizer()
stop_words = set(stopwords.words('english'))
# Function to preprocess text
def preprocess_text(text):
    # Lowercase the text
    text = text.lower()
    # Remove non-alphabetic characters
    text = re.sub(r'[^a-z\s]', '', text)
    # Tokenize the text
    words = text.split()
    # Remove stopwords and lemmatize
    words = [lemmatizer.lemmatize(word) for word in words if word not in stop_words]
    return ' '.join(words)
# Apply preprocessing to the translated_review_text column
new_df['cleaned_review'] = new_df['translated_review_text'].apply(preprocess_text)
# Display the first few rows to verify
print(new_df[['translated_review_text', 'cleaned_review']].head())

As noted, your next step is to then apply the customer sentiment analysis on that information. Let’s assume we are using a BERT model to review that text. Check out this code:

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nlp = pipeline("sentiment-analysis")
# Predict sentiments
reviews = new_df['translated_review_text'].tolist()
results = nlp(reviews, truncation=True)
# Extract sentiment labels and scores
new_df['sentiment_new'] = [result['label'] for result in results]
new_df['score_new'] = [result['score'] for result in results]

You can then visualize the data using the preferred method you selected.

Customer Sentiment Analysis Meaning to Your Business

Customer sentiment analysis in Python is a critical resource that can change the way you make decisions within your business. Getting it right does not have to be challenging. Implementing sentiment analysis in Python helps businesses gain actionable insights into customer opinions, enabling data-driven decision-making and enhanced customer experience strategies. Rayobyte can help you facilitate the process through proxy services. Connect with us now to learn more.

The information in this article is presented solely for the education of the reader. All trademarks used in this publication are hereby acknowledged as the property of their respective owners.