Sentiment Analysis and Machine Learning: A Perfect Match for Improved Marketing Strategy

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Sentiment analysis – together with machine learning techniques – is a powerful tool to boost a brand’s performance and profit from successful customer experiences. 

 

Customer focus often dictates that businesses need to spend big on research to form an effective marketing strategy, from the feedback analysis and competitors’ study to product fit in the new markets. Given that, it’s understandable that data is key in developing strategies, tools and techniques to make a company stand out. With the amount of unstructured data available, any effort to organize, sort, understand, and even  monetize, seems like a daunting task. 

Sentiment analysis is one of the best ways to unlock the massive potential of this information. Using this technology, companies can tap into the great potential of market trends, customers’ attitudes, people’s inclinations and influences. How can businesses effectively embed sentiment analysis algorithms for marketing projects? Let’s explore this matter step by step with Unicsoft’s big data and machine learning experts. 

 

Sentiment Analysis Definition 

 

As the name suggests, sentiment analysis aims to detect sentiments, or the polarity of people’s emotions in the text. It is also referred to as “opinion mining.” Sentiment analysis can be used to analyze any type of text, from customers’ feedback to social media feeds and survey responses, and define the general attitude towards the brand, a new product, pricing changes, customer service, etc. 

Sentiment analysis focuses on: 

  • polarity – positive vs. negative
  • emotions – happy, frustrated, angry, etc.
  • intentions – interested vs. not interested

 

Sentiment Analysis Components

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Sentiment analysis is perfect for processing marketing data. It can be: 

  • rule-based or lexicon-based – a set of rules is developed by the linguists, in which all words are classified as positive or negative 
  • machine learning-based, where ML algorithms are trained to recognize the polarity, emotions and intentions in a supervised, unsupervised, or reinforced manner
  • a hybrid one, which leverages the mix of two approaches 

Let’s define key sentiment analysis applications.

Common Sentiment Analysis Applications in Various Industries 

Sentiment analysis is a technique that supports brand monitoring and reputation management, among other things. Businesses use big data analysis & machine learning to gain a competitive advantage in their business domains. Machine learning is the backbone for accurate sentiment analysis and valid business decisions, from building long-term trends to composing the perfect words to make customers love your product instantly.   

Here are a few, prominent sectors where sentiment analysis is beneficial:

  • Customer experience: consumer voice, brand reputation, e-commerce, advertising.
    Social media analysis is often used to monitor a brand’s reputation on Facebook, Twitter and Instagram. One example is real-time tweet analysis.
  • Politics: voting advice, adjustment of politicians’ programs.
    Campaign managers use sentiment analysis to understand how people feel about certain issues in the politician’s program or rhetorics, how they react to speeches and actions, etc.
  • Public relations: event monitoring, policies, transportation, legal matters.
    By analyzing social media content and news feeds, authorities can optimize traffic flow, ensure better public security and resolve issues before they become too pressing.
  • Finance: financial risk, the evolution of stocks and shares. 

By analyzing articles, news and social media info about public companies, data analysts can assign scores for trading systems – such as Stock Sonar – and form stock prices.

Let’s focus on the business domain and explore how companies leverage sentiment analysis applications. 

Examples of Business Values generated with Sentiment Analysis 

Customer experiences are driving business results these days, causing companies to continue increasing R & D budgets in this field. Sentiment analysis is worth investing in, as it helps decision-making in product development and analysis, marketing campaigns, trend creation, advanced support and many other areas. 

Here’s how businesses leverage this tool. 

Enhanced Brand Loyalty 

Every time a customer mentions a brand, they do it in a specific context and with a personal  intent. Brands should pay attention since instances like these provide valuable insight into the customer’s attitudes and loyalty. Based on this information, companies can tune product features, adjust marketing campaigns, correct mistakes and improve conversions. 

Boost your brand and profit from successful customer experiences.
Implement sentiment analysis into your marketing strategy with Unicsoft custom software development services.
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Recently, Unicsoft developed a big data SaaS solution for a tobacco company (whose name is under NDA). The customer wanted to analyze and predict customer reactions to various types of ads, visual materials, design elements and so on. By leveraging machine learning algorithms, our customer can now define behavior and purchasing activity fueled by promotional activities. As a result, they reported an increase in customer response and, consequently, a revenue increase.

Secured Social Media Reputation

Social media is probably the most potent source of opinions and attitudes and is, therefore, perfect for sentiment analysis. With this automatic tool, one can analyze thousands of comments, tweets and video comments easily, then categorize urgent issues to prioritize necessary improvements. 

Opinion listening across social media channels helps uncover influencers who can support a solid marketing strategy: 40% of customers say they purchased a product after seeing it on Twitter, Youtube or Instagram. By tracking sentiment, a brand can detect influencers talking about their product and engage with their fans as well.

Listen to the Voice of the Customer

Capturing the ‘voice of the customer’ means defining your target audience accurately, formulating a value proposition and changing it according to the needs of your customer. However, there’s no ready solution. The company needs to form customer voice based on various sources across multiple platforms. Sentiment analysis can help capture the “voice of the customer” and sort everything out effectively.    

Comprehensive Market Research 

Whether you are launching a new product or exploring a new market, sentiment analysis can help keep an eye on customers’ reactions. This allows your MVP or product to be changed and improved before it becomes too costly. By segmenting your product’s features through sentiment analysis, you can create marketing campaigns to target certain groups who have shown interest in that specific feature. Also, sentiment analysis allows you to analyze your competitors and use this information to your advantage. 

Winning Customer Service

90% of US citizens consider customer service an essential factor when deciding whether or not to do business with a company. And 49% of Americans switch to competitors because of poor customer service. With the strong influence of social media on the modern consumer, one bad experience can go viral within a few hours. 

With sentiment analysis, businesses can undertake risk management, create emergency plans and provide customer support teams with the best tools to face problems. Mining opinions can also help to understand how people feel at different stages of the user journey and where their biggest concerns lie. 

 

 

Applying sentiment analysis to customer feedback, Unicsoft machine learning experts helped a significant e-commerce business detect the tone and temperament of customers’ social posts and categorize those sentiments. Their business goal was to increase customer loyalty, drive business changes, and deliver real return on investment. As a result, the analytical solution created by Unicsoft professionals assisted customers in developing a data-driven marketing and sales strategy, which resulted in a 10% revenue increase within one year of deployment.

Now it’s time to go deeper into how sentiment analysis and machine learning algorithms actually work.

Key Tasks Performed by Sentiment Analysis when Combined with Machine Learning and AI

Manual processing of tweets, lets say, to rate the sentiment of each statement and then draw various conclusions requires an enormous amount of manpower. Instead, by applying sentiment analysis API, one can create datasets based on certain keywords and then perform text mining. Sentiment analysis runs on top of an AI suite and can leverage machine learning algorithms. The most popular APIs for sentiment analysis are: 

  • IBM Watson Natural Language Understanding
  • Amazon Comprehend
  • Microsoft Azure Text Analytics
  • Google Cloud Natural Language
  • Lexalytics Semantria API

Let’s dive deeper into the tasks that sentiment analysis fulfills. 

 

Sentiment Analysis: Sub-Tasks and Approaches

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Differentiating between factual and opinionated text (Subjectivity Classification)

Subjectivity classification detects various sentiments, emotions, evaluations, etc., based on specific words and context. It is more complicated than determining polarity as various types of text like news, videos or political documents require the identification of both the topic and the attitude holder. So, for subjectivity classification, the algorithms must recognize opinionated language and distinguish it from the objective text. Though ML is more efficient, the lexicon-based approach is used as well. 

 

Determination of positive, negative, or neutral text (Sentiment Classification)

Using ML, one can classify sentiment accurately. There are two approaches, supervised and unsupervised.  

  • In supervised sentiment classification, you have to teach machine learning algorithms how each word influences the overall sentence being positive, negative, or neutral. This requires manually tagged words or libraries of pre-trained NLP models with sentiment scores. 
  • An unsupervised approach presupposes that you provide data, and the AI has to learn the structure of the given data on its own. 

The lexicon-based approach is also used for sentiment classification  – in a dictionary (with positive-negative labels on the words) or corpus-based (defining sentiment in conjunction with a set of positive or negative words it is used with) way. 

 

 

Definition of positive vs negative sentiment (Polarity Determination)

Polarity determination is a primary task of sentiment analysis and can be performed with machine learning, lexicon-based and hybrid approaches. The determination of positive, negative or neutral attitudes is applied for any kind of text, from product reviews to forums and social media. Video and image content can be analyzed also. 

 

Differentiating emotions and sarcasm in the text (Resolving Ambiguity in Opinionated Text)

Since the majority of texts are emotion-colored and include rhetorics, metaphors, sarcasm, comparison, etc., the detection and understanding of these nuances is a challenging task of opinion mining. 

The AI model is trained with specific sets of preprocessed data where words and sentences are given specific scores: positive/negative words, level of pleasantness, a humorous property of the sentence, etc. There are also attempts to “classify” sarcasm and ugliness.  

 

Cross-language analysis (Multilingual Sentiment Analysis)

This kind of sentiment analysis utilizes both machine learning and hybrid approaches. 

In cases where cross-language analysis is needed, machine translation using Google Translator API, for instance, precedes actual sentiment analysis. Also, analysts use labeled lexicons – bilingual dictionaries with polarity or sentiment tags (positive, negative, neutral, offensive, etc).  The text is then classified into datasets and analyzed.  

Detection of fake reviews (Opinion Spam Detection)

Marketing strategies can involve writing fake reviews and piling up positive feedback. Spam comments can be used both to promote a low-quality product or discredit a good one. Spam detection aims to confirm whether a review is written by an actual customer or not. For this to happen, one must analyze three features:

  • text: NLP and machine learning algorithms can uncover deception in the content
  • meta-data: valid user ID, location, IP address
  • real-life knowledge: brand monitoring and competitor analysis, with the help of sentiment analysis 

 

Summing It Up

Recognizing that customer experience drives business performance, brands are taking a smarter approach to market research and sales strategies. Financial institutions and political parties also recognize the importance of collecting and analyzing opinions. 

Combined with machine learning, sentiment analysis is a powerful tool with multiple applications across different industries. It is already influencing the way brands approach marketing, and the impact will be even more visible as AI becomes smarter and ML algorithms become more advanced. By understanding what makes your customers tick, you can resolve the pain points, anticipate wishes and predict problems. 

Do you want to see how Unicsoft can apply the combination of machine learning and sentiment analysis in your business domain to make your brand performance more data-driven & client-oriented? Contact me today! 

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