Best Practices for Sentiment Analysis
The success of any business relies mainly on its customers and how much the customers value the products provided by that business.
However, it’s not always the case that a business gets it right the first time When delivering products to the customers.
Data Science is one of the solutions to ensuring that customer feedback is incorporated into a business’s product or service delivery.
This is where sentiment analysis comes in; huge amounts of data are generated from collecting feedback, comments, and posts that mention a given product. A business collects this data and uses it to understand the customers’ opinions. Through machine learning or text analysis, sentiment analysis results can guide a business in making the right changes or addressing issues related to product or service delivery.
What Is Sentiment Analysis?
Sentiment analysis helps the business categorize or understand the customer’s emotions towards the products or services the business delivers. Simply put, sentiment analysis gives a polarity score on how the customers feel about a given aspect being tested about the business. In some cases, sentiment analysis is known as opinion mining.
Most sentiment analysis is done on feedback collected about a business, its social media engagement, or its campaigns. Businesses can carry out campaigns collecting user feedback on social media and later analyses the collected data to understand the sentiments behind the data.
Steps in sentiment analysis
Like any other data-related project, sentiment analysis follows the data analysis and machine learning process. The first steps are data collection. With most of the data coming from either feedback or social media campaign data, data scraping is the best approach to getting the necessary data for sentiment analysis. Data scraping can use any of the tools that enable data scraping. Using their API is the best way to get the necessary data for social media platforms such as Twitter. Twitter tweepy API is a good place to start.
The next step is data preprocessing. In the case of sentiment analysis, data preprocessing entails text preprocessing, vectorization, and tokenizing of the text data. Tools such as NLTK are essential in this stage and help you do the preprocessing task easily. Activities involved in this stage include removing stop words and lemmatizing the data.
At this stage, you can use the resulting data to visualize a word cloud that shows which words frequently occur in the data and gain insights into what people are talking about regarding your product. It is also a good place to see some adjectives that customers use to describe your product.
The last step is to model the machine learning model using logistic regressions, Naïve Bayes, or support vector machines. Sentiment analysis is a classification problem. Once this is done, you can visualize the results and determine what polarity most o the communication or texts fall on as it relates to the product.
Note: you can use real-time streaming data to score the different texts that come in based on their polarity and use this to plot a graph to show how the sentiments on your product are trending throughout the period. Streaming your social media account data helps you have a real-time view of your customers’ sentiments about your brand or product.
Best Practices For Sentiment Analysis
When performing sentiment analysis, you can either build your tool or purchase a tool to do the sentiment analysis. The best option for a tech-savvy business is to build its tools and use them to gauge the sentiments; however, if you don’t have the right team, purchasing SaaS sentiment analysis tools might be the available alternative.
These out-of-the-box tools can be integrated with other tools, such as CRMs. However, having an in-house team that builds the tools or models to perform sentiment analysis gives you full control of the activities and over what data you want to be analyzed.
Most popular application areas for sentiment analysis
Data is constantly growing, and businesses need to keep up. The most popular application for sentiment analysis is understanding unstructured and bulk company data. Understating unstructured data requires the business to utilize machine learning technologies to categorize the data into various categories.
One of these categorizations is on customer data’s sentiments or polarity. By polarity, I mean where the feedback or review is positive, negative, or neutral. Sentiment analysis also provides a score on either side of the polarity extent. While understanding the data in its raw format is hectic, having the data categorized based on polarity is simpler and more actionable.
Social media is a tool for growth and a weapon against failures in business services. Today, when a business does something wrong, almost everyone runs to social media to criticize it. The same happens when your delivery does not arrive on time, and you rush to Twitter to tweet about it. Sentiment analysis helps businesses to take fast action on customer complaints on social media by gauging the experience using sentiment analysis.
Similarly, when a business needs to establish a competitive advantage, understanding the sentiments of the market or the customers is essential. Sentiment analysis can also help in predicting what future customers will do and the overall customer trend on a given item or product.
Sentiment analysis on products or a business helps build significantly better business intelligence. The insights about sentiments and unique words describing a product or a business help the business make the right decisions on strategizing and addressing issues it might have.
The insights can also give the business the right ideas for targeting a market demographic. An example of this is when a product is being reviewed positively by a given demographic group A while a different group, B, has a negative sentiment toward the product, the business can easily identify the issue raised by group B and address them to win the demographic group as a market.
Challenges in sentiment analysis.
Anyone who has been involved in social media campaigns knows that to increase a post’s visibility, some campaigners use trending keywords, thereby increasing not just their visibility but that of the product in question. This is beneficial in that more people will learn about the product.
However, at the same time, the posts that will come up on the trend of the product will not be related to the product. This is a big issue with the sentiment analysis of social media data sources. Most of the data can be perceived to be similar to Dark data. The dark data is collected from the campaign but has nothing to do with the actual campaign or product, and therefore it will affect the sentiment analysis results.
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