Business and Social Media Sentiment Analysis

In a competitive market where corporations compete for customers; Patron satisfaction is seen as a significant difference. Sentence analysis in business, also known as opinion mining, is a process of identifying and listing a piece of text according to the tone communicated by it.

Relationship between Customer Emotions and Business Success

Customer satisfaction is the most important indicator of how the customer will make a purchase in the future. Businesses that succeed in those low-throat environments make customer satisfaction a key element of their business strategy. Sentiment analysis in business can prove to be a major success for complete brand revitalization.

Finding customers’ feelings about a product

Most social sites offer popular target market metrics, however, achievement is additionally dependent on actionable statistics that can be ascertained through target audience participation. Sources of reviews are mainly sites, eg Twitter, Facebook etc.

Business Target Sentiment Analysis

It is important to understand the overwhelming, poor, and even unbiased opinion traits. The secret to running a successful commercial with emotion data is the ability to exploit unstructured data for actionable insights. Experts agree. According to a customer experience visionary Bruce Temkin, “emotional” is one of the three key experience components.

The challenges of emotion analysis

The challenge is to properly measure emotion and turn findings into actionable customer-experience strategies. Sentence analysis through ‘text-analysis’ has been part of market-leading solutions for many years.

Research work on sentiment analysis

Sentiment analysis is a continuous area of ​​research. The research focuses on the computational treatment of opinions, feelings, and textual content. Many researchers are aiming to propose a highly accurate classification algorithm to extract the feelings of texts. Research states that classifying text at the document level or at the sentence level does not provide the necessary information on all aspects of the unit that is necessary in many applications.

How does the rise of a language and cultural barrier create complexity?

Technological methods usually first begin with a lexicon that assigns phrases – “good,” “fast,” “expensive,” “hot” – to the positive and negative categories. Problems arise with the complexities of the use of the word. This coffee is hot (usually) a good thing, while a hot room is not, and “hot” in “hot chocolate” is only descriptive.

Add words like “no”, it means the opposite, “a lot”, modifiers and idioms, metaphors, abbreviations, and worlds like emoticons – plus language and cultural complexities – and you face a complex analytical challenge.

Incorporation of machine learning into complex lexicons in a language

Machine learning has a solution for this. Focus in aspect-level is necessary to address these details. The aspect-level aims to classify emotion in relation to specific aspects of entities. The dataset used in sentiment analysis is an important issue in this area. The main sources of data are from product reviews. These reviews are important for business holders as they can make business decisions according to the analysis results of users’ opinions about their products.

Where zenser stands

With our machine learning capability at Zenser, we have helped our customers build a sentiment analysis system that helped improve their business. For more information, please write to us at marcom@zensar.com

In the next blog, we will focus on how the implementation of sentiment analysis is done in real-world data and using different techniques.

Some customers report having high latency between their architecture in AWS and local databases. It is sometimes found that the service database is responsible for high latency and not distance.

This issue can be resolved by moving the database to Amazon Relational Database Service (RDS) and creating an RDS read-replica, however, the licensing scheme of the database prevents it from moving to AWS. Another challenge when it is transferred is integration between the application and the database since the production in the system and it is not possible to rewrite the entire application in a separate database engine where it has no license restrictions.

One mechanism to optimize its operation is to use cache in memory using Amazon ElastiCache to reduce the load of your database. This will also help improve response time as the cache will be inside the VPC. This will provide an improvement in experience for the end user.

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