Sentiment analysis is the process of identifying, determining, and placing opinions in categorical form through a computational medium. The purpose of this analysis that is often expressed in text format, is to determine the attitude of a writer towards a particular matter, brand, or product, etc. The sentiment places the writer’s to be positive, negative, or neutral.
The work of sentiment analysis originally involves collecting a term or text, the text may be a sentence, a comment, and sometimes an entire document, after collection, it analyses it and returns a score or result that decides how positive or negative the text is. For example, when a product is been published or advertised, the response or feedback of customers can be placed in a scale of sentiment analysis that will measure the reaction of customers towards an area of the service or brand which they describe in text.
In today’s market system where digital markets are cumbered with loads of data (albeit data overload is not equal to palatable or deeper insights), startups might have numerous customer feedbacks collected; judging from human capacity, it is impossible to analyze such data manually without any sort of error or predilection.
Most times, brands with the ideal motives find themselves in a space where their insight is deteriorating. In your decision making (mostly digital marketing), you need to understand your insights in order help you in the aspect of your decision making because when you lack insight, you might know that you are not engaging customers, but you might not know how best to get them, which is why the sentiment analysis is needed.
Sentiment analysis easily gives some answers to the most essential issues in business and brand promotion, in the area of customers feedback, and engagement. Sentiment analysis can be digitally automated, this automation is beneficial because decisions can be made based on a significant number of data, and not just guesswork that might not be right always.
The need for sentiment analysis
Sentiment analysis is needful for easily getting insights through the usage of large volumes of text data. Also, it can be used in the customer feedback analysis, which was discussed above. In stock trading, many companies are always in the internet searching for news that might benefit their startup, with regard to this, the sentiment algorithms also detect particular companies whose trawling shows a positive sentiment in most news articles. This phenomenon can result in a significant financial advancement, as this simple detection may trigger audiences to buy more of the company’s product. Also, if marketers have access to this type of data, may give them the upper hand to make decisions in the system before the market has time to react.
When brands want to decrease customer agitation, using sentiment analysis can help tremendously to focus on the feedback of the customers, word to word in areas where the sentiment is ardently negative. Also, the sentiment analysis can be used to observe customer’s comments, pick with positive sentiment, then critically find out why these customers love the brand. After observation and understanding the reasons behind the positive sentiment, the brand will then focus on what ought to be done to the business in order to increase the number of brand promoters.
With stated examples above, it is evident that sentiment analysis is used by taking a source of data in text format then narrow it down to decision making. This has a lot of benefits in customer experience.
Techniques for sentiment analysis
The rules-based technique is the first technique. This method is achieved by the use of a dictionary of words that are well labelled by sentiment to observe and determine the sentiment of a sentence. In order to make this achievable, after getting the score of sentiment, the scores will be combined with some extra rules to reduce sentences containing negativity, derision, or clauses that are dependent.
2. Sentiment by Machine Learning.
In machine learning, the system trained with a model that recognizes the sentiment based on the words and their definite order using a sentiment that is dependent on the basic training set of the system. This type of approach is largely dependent on the particular type of algorithm and also the quality of the training and modelling of the used data.
One reliable pattern that can be followed to make this approach fit various types of problems is to measure separation and alignment of a problem with other dimensions. For instance, emotions can be observed and related to other phenomenons; anger can influence people and affect them when they are sending messages through text, even fear and anxiety can be conveyed through text. So the Machine is trained to understand emotions through words that are used in a text.
Sentiment Analysis: the good side.
The benefit of sentiment analysis is truly worth a hype, it helps brand owners to measure how customers feel about diverse areas of their brand without the stress of reading tons of customer comments and feedback at once.
Let’s take, for instance, if you are encumbered with thousands, or tens of thousands of survey responses every month, it is definitely not possible for you to read all of these responses and have an accurate and dependable measure of customer sentiment. The idea of using automated sentiment analysis in such a process will help you to easily break down into different customer ideas of your startup and get a better comprehension of sentiment in these customer ideas.
Sentiment analysis: the bad side.
Despite the benefits of sentiment analysis, it does not completely rule out the essence of literally reading of survey response, neither does it stand as a complete replacement for reading survey responses, because there is often useful subtlety in the comments themselves. Irrespective of the ability of the sentiment analysis, it helps to identify areas of a comment that should be read, for instance, it gives way for users to focus on the most negative comments.
Basic sentiment analysis is done by using reference dictionaries to determine how positive distinct words are and then use the observation in calculating the average of the scores as the sentiment of that text. The next pace from here is using a simple Machine learning model that classifies the sentiment.