- Relationship between sentiment analysis, Data mining, and Text mining
Data mining refers to the process of extracting information from big data is known as data mining. It’s managed here, analyzed, and presented to the user. The user then analyzes raw data so that one can conclude the given information (Bhushan, 2018).
Text mining refers to the process of transforming unstructured data into meaningful information that actions regarding decision making can be made easily using artificial intelligence.
Sentiment analysis the process of people’s opinions and attitudes towards a specific topic or product using various automated tools. (Sharda, Delen, & Turban, 2020).
The relationship between the three is that they all seek to extract information from big data and identify a useful pattern that can be used in decision making. They are also semi-automated.
- Application of Text Mining
Text mining transforms unstructured data into meaningful information that actions regarding decision making can be made easily using artificial intelligence.
It’s mostly applied in risk management, marketing, academics, cyber-crime prevention, security, business intelligence, and social media data analysis.
- Induced structure
Big data is most complex and getting unstructured data into a format that is easy and more effective for analysis so that the information got can be beneficial to a business. Induce structure into a text-based data is crucial in solving this. It enhances data capturing, time-sensitivity of the data, and improvement of customer relations (Sharda et al., 2020).
Ways of inducing structures
The first way is to isolate keywords so that you analyze the area of interest. It helps to know the words that are of great importance for analysis, which helps in determining the acts of the keywords (Andrew, Samia, & Ednre, 2014).
The second way is to determine the topics so that you can categorize the subject matter since you already know the content, and the last way is to measure sentiments to gauge the tone whether the data is positive or negative (Andrew et al., 2014).
- Role of NLP
Natural language processing helps in structuring a collection of text from unorganized data to data that can be easily classified, clustered, and associated. (Sharda et al., 2020).
Capabilities
NLP converts sentences with a different meaning to real understanding, making it easy for analysis and processing (Sharda et al., 2020). It plays a massive role in artificial intelligence.
Limitations
NLP is faced with the following challenges:
- Marking up terms in the text
- Lack of word boundaries
- Word with more than one meaning
- One sentence having many meanings
- Foreign accent
- Poor sentence structure
Application 7.8
Use of social media analytics in the consumer product industry
Social media has many users, and using social media analytics is of great importance since it helps in identifying the target market, and appropriate strategies in marketing are quickly adopted. It helps companies know common motivations from consumers, and hence management can quickly come up with products that will have a ready market. Analyzing consumer’s conversations in social media helps in saving brands retroactive efforts in innovation. It’s the best place to get answers on how consumers will adapt to the change of products and how demand and supply will be balanced (Sharda et al., 2020).
Proper implementation of social media analysis enabled the shaping of shelves with more products by allowing customers to give views on the brands they require. When products are made, they are posted online, and potential clients can easily make recommendations on making the product better.
Key challenges, potential solutions and probable results in social media analytics
The main challenge that social media analytics encounters are coping with the speed of gathering information. Social media platforms can be accessed by potential clients 24/7, and hence companies must be up for the task and ensure that someone is following customer’s conversations at any given time. Companies, therefore, require to hire people who will work in shifts to ensure that there is always someone answering client’s questions to increase the return they get from their product.
Lack of consistency in the implementation of social media analytics is also a considerable challenge. Management should not take any information they get lightly to ensure that the companies always innovate new products. It will result in shaping companies’ shelf so well, and growth can be easily seen.
Another challenge in using social media analytics is the failure of institutions to understand potential markets. Management should there understand that social media gives market surveys from a global perspective. New products that meet these markets can be made, and international expansion will be fast.
References
Andrew, S., Samia, T., & Ednre, T. (2014). Inducing Information Structures for Data-driven Text Analysis.
Bhushan, A. (2018). How Big Data Impact Smart Cities.
Eldersveld, D. (2016). Solutions to bring structure to your text data.
Sharda, R., Delen, D., Turban, E. (2020). Analytics, Data Science, & Artificial Intelligence: Systems for Decision Support