Text Analytics is a branch of AI that uses NLP to create structured data by converting unstructured text from documents and databases. This data is used to power learning algorithms or for analytic purposes. Text analytics also extracts and classifies actionable insights from social media, emails, and customer feedback.
What Are the Applications of Text Analytics?
Text Analytics plays an indelible role in:
• Monitoring brand reputation
Text analytics evaluates large data volumes from social media posts and product reviews. The real-time analysis helps businesses track and address customers’ negative feedback and uphold their brand reputation and public perception.
• Enhancing customer service
Businesses use text analysis to improve their efficiency in serving their customers. The technology analyzes customer feedback and queries to reveal insights that allow organizations to promptly identify and address the issues and deliver an efficient customer experience.
• Gathering business intelligence
Text analysis unravels the reasons for specific trends and patterns by assigning context to numerical data. For instance, the technology helps explain the reasons driving the change in sales performance, while data analysis only shows the change.
• Improving operational efficiency
Text analytics effortlessly processes and extracts valuable information in data-intensive fields like healthcare. It decodes extensive medical records, patient feedback, and medical research papers to extract vital insights to support decision-making processes. These decisions eventually improve the operational efficiency of the institution.
• Scrutinizing incoming emails
In the realm of cybersecurity, text analytics uses advanced algorithms to survey and segregate incoming emails for spam, malware, and phishing content. This process substantially minimizes the risk of cybersecurity attacks and augments overall digital security.
FAQs
1. How does Text Analytics work?
Text analytics processes unstructured text using NLP techniques like:
- Tokenization
- Parsing
- Stemming
- Lemmatization
- POS tagging
- Stop Word removal
- Text normalization
These processes simplify the text into manageable units and prepare the data for machine analysis.
2. What are the major hurdles in text analytics?
Text analysis may present challenges due to its lack of fixed structures and its magnitude, which often exceeds the numeric data size.
Also, its unstructured nature requires more sophisticated algorithms to identify sentiments, meaning, and patterns within the diversely styled and formatted textual content. The requirements increase the processing complexities, necessitating advanced NLP techniques and ML models to draw actionable insights from text data.
3. What is the preferred language for text analytics?
Generally, the preferred language for text analysis depends on the project requirements, the complexities, available tools, and the team’s expertise. Here’s a list of languages that fit the bill:
- Python for its libraries like Scikit-learn, NLTK, and spaCY. This language also supports NLP procedures and is simple and readable.
- R for data visualization and statistical analysis.
4. Are NLP and text analysis the same?
Although NLP and text analysis are related, they are distinct avenues within Artificial Intelligence. NLP uses tasks like language understanding and generation and speech recognition to enable computers to interpret and generate human language. In short, NLP encompasses multiple linguistic tasks beyond text analysis.
Text analysis, on the other hand, is a specific NLP-reliant application that analyzes, processes, and derives information from text data.
5. How is Text Analytics integrated into business operations?
When integrating text analytics into business operations, companies must decide between developing software from scratch or adopting a ready-made SaaS solution. Also, the duration and cost of integration vary according to the scale and complexity of the process.
For a successful integration, organizations should:
- Hire data scientists and engineers
- Choose the right tools
- Estimate the development time.
Related Terms
