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Businesses will have mastered the acquisition of consumer interaction data by 2021.
Over-reliance on these data points, on the other hand, frequently leads to organizations treating customer input as a statistic – a rather one-dimensional approach to listening to the customer’s voice.
The customer’s voice cannot be badged or converted into a number.
It must be read, condensed, and, above all, comprehended.
The fact is that companies must actively listen to what their consumers have to say on every channel through which they interact with them, whether it’s through phone calls, emails, or live chat.
Every company should prioritize monitoring and evaluating consumer feedback sentiment, but companies have traditionally struggled to handle this data and transform it into meaningful intelligence.
This is no longer the case with Sentiment Analysis.
In this tutorial, we’ll take a closer look at sentiment analysis, its advantages, and how to use the NLTK library to do sentiment analysis on data.
What is sentiment analysis?
Sentiment analysis, often known as conversation mining, is a method for analyzing people’s feelings, thoughts, and views.
Sentiment analysis allows businesses to gain a better understanding of their consumers, increase revenue, and enhance their products and services based on client input.
The difference between a software system capable of analyzing customer sentiment and a salesperson/customer service representative attempting to deduce it is the former’s sheer ability to derive objective results from the raw text — this is primarily accomplished through natural language processing (NLP) and machine learning techniques.
From emotion identification to text categorization, sentiment analysis has a wide range of applications. We employ sentiment analysis on textual data to assist a firm monitor the sentiment of product evaluations or consumer feedback.
Different social media sites use it to assess the sentiment of postings, and if the emotion is too strong or violent, or falls below their threshold, the post is either deleted or hidden.
Sentiment analysis can be used for everything from emotion identification to text categorization.
The most popular use of sentiment analysis is on textual data, where it is used to help a company in tracking the sentiment of product evaluations or consumer comments.
Different social media sites also use it to assess the sentiment of postings, and if the emotion is too strong or violent, or falls below their threshold, they delete or conceal the post.
Benefits of Sentiment Analysis
The following are some of the most important benefits of sentiment analysis that should not be disregarded.
Help in assessing the perception of your brand among your target demographic.
Direct client feedback is provided to help you in developing your product.
Increases sales revenue and prospecting.
Upsell opportunities for your product’s champions have increased.
Proactive customer service is a practical option.
Numbers can provide you with information like the raw performance of a marketing campaign, the amount of engagement in a prospecting call, and the number of tickets pending in customer support.
However, it will not tell you why a specific event occurred or what caused it. Analytics tools like Google and Facebook, for example, can help you assess the performance of your marketing efforts.
But they don’t provide you with an in-depth knowledge of why that specific campaign was successful.
Sentiment Analysis has the potential to be game-changing in this regard.
Sentiment Analysis – Problem Statement
The aim is to determine if a tweet has favorable, negative, or neutral emotion regarding six U.S. airlines based on tweets.
This is a standard supervised learning job in which we must categorize a text string into predetermined categories given a text string.
We’ll use the standard machine learning process to address this problem. We’ll start by importing the necessary libraries and datasets.
Then we’ll perform some exploratory data analysis to determine if there are any patterns in the data. Following that, we’ll undertake text preprocessing to turn textual input numeric data that a machine learning system can use.
Finally, we will train and evaluate our sentiment analysis models using machine learning methods.
1. Importing Libraries
Load the necessary libraries.
2. Import Dataset
This article will be based on a dataset that can be found on Github. The dataset will be imported using Pandas’ read CSV function, as seen below:
Using the head() function, examine the dataset’s first five rows:
3. Analysis of the Data
Let us examine the data to determine if there are any trends. But first, we’ll change the default plot size to make the charts more visible.
Let us begin with the number of tweets received by each airline. We’ll use a pie chart for this:
The percentage of public tweets for each airline is displayed in the output.
Let’s have a look at how the feelings are distributed over all of the tweets.
Let us now examine the distribution of sentiment for each specific airline.
According to the results, the bulk of tweets for nearly all airlines are unfavorable, with neutral and good tweets following. Virgin America is perhaps the only airline where the proportion of the three feelings is comparable.
Finally, we’ll use the Seaborn library to get the average confidence level for tweets from three sentiment categories.
The result shows that the confidence level for negative tweets is greater than for positive or neutral tweets.
4. Cleaning the data
Many slang terms and punctuation marks can be found in tweets. Before we can train the machine learning model, we need to clean our tweets.
However, before we begin cleaning the tweets, we should separate our dataset into feature and label sets.
We can clean the data once we’ve separated it into features and training sets. Regular expressions will be used to do this.
5. Numeric Representation of Text
To train machine learning models, statistical algorithms employ mathematics. Mathematics, on the other hand, solely works with numbers.
We must first transform the text into numbers for statistical algorithms to deal with it. There are three basic ways of doing so: Bag of Words, TF-IDF, and Word2Vec.
Fortunately, the TfidfVectorizer class in Python’s Scikit-Learn module can be used to transform text features into TF-IDF feature vectors.
6. Creating Data-Driven Training and Test Sets
Finally, we must divide our data into training and testing sets before training our algorithms.
The training set will be used to train the algorithm, and the test set will be used to assess the machine learning model’s performance.
7. Model Development
After the data has been separated into training and test sets, machine learning techniques are used to learn from the training data.
You can use any machine learning algorithm. The Random Forest approach, however, will be used because of its ability to cope with non-normalized data.
8. Predictions and Model Evaluation
After the model has been trained, the final stage is to make predictions. To do this, we must apply the predict method to the RandomForestClassifier class object that we trained.
Finally, classification measures like confusion metrics, F1 measures, accuracy, and so on can be used to evaluate the performance of machine learning models.
Our algorithm achieved an accuracy of 75.30, as seen by the results.
Sentiment analysis is one of the most frequent NLP jobs since it helps identify overall public opinion on a specific issue.
We saw how several Python libraries can help with sentiment analysis.
We conducted a study of public tweets about six U.S. airlines and reached an accuracy of roughly 75%.
I would suggest that you try another machine learning algorithm, such as logistic regression, SVM, or KNN, to see if you can achieve better results.