Quite often, you have come across situations where you really need to understand the intent behind a message and you’re completely lost. That is the time you need sentiment analysis to come into play.
Sentiment analysis which is also referred to as opinion mining is an approach to natural language processing (NLP) that identifies the positive, negative, or neutral tone behind a body of a text. It entails the use of data mining, machine learning (ML), and artificial intelligence (AI) to mine text for emotions and subjective information.
Sentiment analysis systems help brands to gather insights from unorganized, confusing, and unstructured texts that come from online sources such as emails, blog posts, support tickets, web chats, social media channels, forums, and comments. Mathematical calculations replace manual data processing by implementing rule-based, automatic or hybrid methods.
Rule-based systems carry out sentiment analysis based on predefined, labor-intensive means while automatic systems learn from data with machine learning techniques. A hybrid sentiment analysis combines rule-based and automatic systems.
Different types of sentiment analysis
- Fine-grained sentiment analysis: provides a more clear-cut level of segmentation by breaking it down into further categories, most often, however, this is broken down into basically very positive or very negative.
- Emotion detection: identifies fixed emotions. Examples could include happiness, joy, comfort, frustration, shock, annoyance, anger, and sadness.
- Intent-based analysis: recognizes the real action behind a message as well as the opinion.
- Aspect-based analysis: pinpoints the particular component being positively or negatively mentioned.
Sentiment analysis can be used to improve your marketing scope in the following three ways.
1. Identifying brand awareness
Being able to identify your brand awareness, reputation, and popularity at a specific moment or over time keep you on top of the game. It’s your key to consumer behavior, advertising management, brand management, and strategy development.
Your consumers’ ability to recognize or recall your brand is very vital and central to their purchasing decision-making. If your consumers do not recognize a product category and brand within that category, they will definitely not carry out purchases.
It affords you the opportunity to measure brand rejection, brand preference, and brand loyalty.
To measure brand awareness, you can use any or a combination of the following metrics.
- Surveys: Enable you to find out how existing customers heard of you or ask a random selection of people if they are familiar with your brand.
- Website traffic: The direct channel in Google Analytics tracks the number of people who typed your URL into their address bar, used a browser bookmark or clicked a link in an untracked email or offline document.
- Search volume data: Use Google Adwords Keyword Planner and Google Trends to check the volume of searches for your brand name.
- Social listening: Listen into online, organic conversations about your brand across social media and the web.
2. Tracking consumer reception of new products or features
It shouldn’t surprise you that different people, no matter how similar they are, make different purchasing decisions. That’s why you must track your consumers’ behavior.
Consumer behavior lets you into the many reasons (personal, situational, psychological, and social), consumers shop for products, purchase and utilize them, and even why they dispose of them eventually. Researchers have even gone to the extent of looking at people’s brains by having them lie in scanners and asking them questions about different products.
This is to compare what people say about the products to what their brains scans show which at the end of the day is, what they are really thinking. The whole idea about scanning people’s brains for marketing purposes might sound unethical but given that just 40% of them were still sold three years later may be a just cause.
When you know what your consumers say and think about your product, you’re better armed when embarking on innovation, rebranding, quality top-ups, and service.
3. Pinpointing the target audience or demographics
While it is relatively simple to develop general advertising for the masses, devoting time and resources to identify more targeted markets will go a long way in ensuring you maximize your marketing ROI. With sentiment analysis, you easily discover your target audience, people who share similar demographic characteristics such as age, gender, income, and level of education.
Once you know who and when to market your product or service, you are almost home safe and dry. This involves implementing systems, rather than relying on indiscriminate marketing.
Forbes reports that close to 50% of millennial women, purchase clothes more than twice a month as against only 36% of women from older generations. Insights like this are what you curate from sentiment analysis to further strengthen your competitive advantage.
Challenges with sentiment analysis
Sentiment analysis can be bogged down with inaccuracies in training models. Bland comments that portray a neutral sentiment, tend to pose a problem for systems and are often misidentified.
A customer that received the wrong size of shoes and only volunteered something like “the size was 42 inches,” in a comment has not really shed much light. The comment may be viewed as neutral when it actually should be negative.
Sentiment analysis also comes unstuck when systems cannot understand the context or tone. In polls or surveys, answers like “nothing” or “everything” are difficult to designate into positive or negative especially if the context is not clarified. Computer programs have also been discovered to have problems when they come across emojis and irrelevant information.
Neutral data and emojis should be carefully studied in order not to inappropriately flag texts. Sentiment analysis hits the brick wall when people tend to be contradictory as most reviews project both positivity and negativity.
Though this shouldn’t scare you away from implementing sentiment analysis. You just go about analyzing one sentence at a time.