How Do You Write a Sentiment Analysis Report?
These days, the need for understanding human emotions is greater. With people becoming more and more vocal, companies have started taking client concerns more seriously. Companies are using feedback and social media platforms to understand people’s sentiments in real-time.
However, the process isn’t easy. Understanding positive and negative sentiments aren’t easy for machines. Thus, we derive an emotion analysis mechanism. Sentiment analysis can help us generate detailed reports about the opinions of an individual. Still, before diving in depth, we must understand how do you write a sentiment analysis report.
Until a person isn’t aware of the methodology, he cannot perform correct analysis on any dataset.
What is a Sentiment Analysis Report?
Before moving on to how do you write a sentiment analysis report, we must understand what is meant by a sentiment analysis report. Opinion mining commonly refers to analyzing emotions and opinions in a piece of text. You could take feedback, reviews, or comments and analyze them. Each sentence and word either contains positive or negative emotions. At times, a person’s words can also have neutral emotions. All these emotions help find the overall emotion in the given dataset.
Once every part is analyzed, you can generate reports to see the final results. These reports show the number and intensity of negative sentiments and positive opinions. It will give a visual and statistical representation.
Moreover, it determines the text’s polarity. Using the results, companies can improve their quality.
How are Analysis Reports Generated?
Generating a report comprises many steps. It starts with comparing incoming data against a list of words. These words act as a dictionary for the machine and are labeled negative and positive. New sentences are compared with the labeled data to interpret the intensity of hidden emotions.
After analysis, we get individual scores. If the score is positive, the emotion is labeled positive. Similarly, for negative scores, the result is negative. When the analysis is complete, the time comes to calculate the final score. The final score signifies the overall emotion in the data. All these statistics are present in the analysis report.
Building Reports Using NLTK and Altair
Analyzing structured data is simpler. You have more clarity. The same isn’t for unstructured data. Thus, we must perform data sorting before analysis. We use data preprocessing and various cleaning techniques to manage our data. We perform the following functions:
- Sorting headings
- Removing null values
All of this is possible using pre-defined libraries. NLTK and Altair are two libraries that reduce our workload and help us visualize results. With Altair, we can use histograms to show the distribution of emotions. It allows us to draw other plots as well. The best thing about a library is that it gives us various types of results with just a change of a few keywords.
If you are worried about how do you write a sentiment analysis report, no need to panic. These efficient systems handle all the steps. You only need to click a few buttons to get results.
Understanding Emotional Trends in Reports
A report provides you with multiple figures, graphs, and statistics. It might be overwhelming, but once you interpret it, things become clear. You should find positive and negative calls in the report. The top part of the report will allow you to select calls for a specific duration of your choice.
If negative values are in a higher ratio, the interpretation will be that the dataset is negative. In case, you have more positive values, the overall polarity of the text will be positive. You can also understand the intensity of emotions present in the text.
Using Sentiment Score
Finding the polarity or intensity of emotion depends on the sentiment score. The higher the sentiment score, the more positive a piece of text is. Similarly, negative scores imply that your given dataset has negative emotions. Usually, the score ranges from -10 to 10, but the numbers can vary. The mechanism breaks down sentences into tokens and analyzes each part separately. Capital letters and sarcasm is taken into account to judge the emotion hidden in a line. Other factors, like context and strength of emotion, influence the results. The algorithm calculates individual sentiment scores and then finds an average value.
Positive Sentiment and Negative Sentiment
Human beings undergo multiple emotions. Maybe you like one product and dislike another one. Depending on your experience, you provide a review. Your feedback contains positive and negative emotions, interpreted through opinion mining. Getting insights about emotions is not possible without this mechanism.
Identifying Neutral Sentiments
At times reviews can have neutral sentiments. In such cases, the overall score will be zero because there is no polarity. Sentiment Analysis models can identify all kinds of emotions.
NLP and Opinion and Mining
Artificial intelligence has helped us in multiple ways, and opinion mining is an ideal example. Processing thousands of records is not possible for any human. Therefore, we take the help of machines. Sadly, computers cannot deal with text analytics without NLP. Natural language processing powers the complete analysis process. If someone is experiencing sorrow, happiness, or anger, you can use NLP to identify the exact emotion. A machine can only understand processed language.
Rule-Based Sentiment Analysis vs. Automated Analysis
There are several approaches to opinion mining, but the most popular ones are:
It is the traditional way of generating sentiment reports. You utilize various NLP techniques to get your desired results. It involves tokenization, data preprocessing, and much more. These processes help translate human language into machine-understandable code. The next step is to score words based on the hidden sentiment. For example, the word happy describes a positive emotion and gets a higher score. You can get an accurate score only when you cater to negations and capital letter words. Lastly, we get an overall score.
Apart from rule-based sentiment analysis, we also have machine learning opinion mining. It relies on machine learning techniques and algorithms. However, before the process initiates, the data has to be cleaned. It is the feature selection process. The preprocessing is similar to the one in rule-based analysis. However, vectorization is an additional process. After feature extraction, we move to data training to predict relevant outcomes. Based on these predictions, we label data. Thanks to the automated process, there is no need for pre-defined lexicons.
Algorithms Used to Analyze Emotions
Algorithms help carry out the entire analysis process. If you don’t use algorithms, you cannot perform opinion mining, or the task becomes unimaginably lengthy. Thus, we use various algorithms, like:
Naïve Bayes: When it comes to the Naïve Bayes algorithm, you get results in the form of probabilities. Each feature is assumed to be independent. Thus with limited data, one can get accurate results.
Linear regression: The algorithm is effective and gives outcomes in binary form.
SVM: Using support vector machines allow you to plot data in multidimensional space. One side of the graph gives negative values, and the opposite side is for positive emotions.
These are some algorithms used in a majority of analysis mechanisms. You can use them to generate reports and results according to your need.
Where is Opinion Mining Used?
Several industries use opinion mining to boost their performance. You can find it in the health, e-commerce, and tourism industry. The most prominent places where opinion mining comes to use are:
Sentiment analysis gives you a complete view of your brand standing in the market.
You can analyze your position in the market with the help of emotion analysis. It helps you answer questions like whether customers like your product or not, why they prefer competitors, and much more. Based on these reactions, you can formulate new strategies for your business.
Nothing can be more effective than opinion mining for understanding customer sentiment. You can analyze feedback, reviews, and comments. It helps you direct your time toward solving client complaints.
Once you are clear about how do you write a sentiment analysis report, you should start using it for the benefit of your company.
Getting the Best Assistance for Your Firm
Running a company is as exhausting as starting one. You need to look over finances, marketing, and strategy-making. It can become a stressful job, but VizRefra is here to help you. You can use our sentiment analyst services or back office support to manage your firm. Connect with us today to explore our range of services.