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Proceedings of the 4th EUROPEAN COMPUTING CONFERENCE Text Analytics as a Form of Knowledge Mining HELLO PAN IAN The Faculty of Economics and Business University of Zagreb J.F. Kennedy Sq. 6 CROATIA

How to fill out text analytics as a

How to fill out text analytics as a:

1. Start by identifying the specific goals and objectives you have for utilizing text analytics. This could include extracting insights from customer feedback, analyzing social media sentiment, or improving the efficiency of text-based data processing.
2. Choose the right text analytics tool or software that aligns with your requirements. Consider factors like the level of automation, scalability, and customization options available.
3. Clean and preprocess your text data to ensure high-quality results. This involves removing noise, normalizing text, and handling any missing or inconsistent data.
4. Select the appropriate text analytics techniques based on the nature of your data and the insights you are seeking. These techniques can include sentiment analysis, entity recognition, topic modeling, or text classification.
5. Apply the chosen text analytics techniques to your data. This may involve training machine learning models, setting up rules-based systems, or using pre-trained models for specific tasks.
6. Interpret and analyze the results generated by the text analytics process. This includes extracting meaningful patterns, trends, or insights from the processed text data.
7. Communicate the findings and insights to relevant stakeholders in a clear and concise manner. Use visualizations or reports to present the information effectively.

Who needs text analytics as a:

1. Businesses across various industries can benefit from text analytics. Customer feedback analysis helps companies understand consumer sentiments, improve products/services, and enhance customer experience.
2. Market researchers can leverage text analytics to gain insights from social media conversations, online reviews, or survey responses. This helps in identifying trends, sentiments, and consumer preferences.
3. Government agencies can use text analytics to analyze public opinion, sentiments, or extract information from large volumes of textual data.
4. Healthcare organizations can apply text analytics to analyze electronic health records, medical literature, or patient feedback to improve healthcare outcomes and decision-making.
5. Media and advertising companies can utilize text analytics to monitor brand perception, track social media trends, or gauge the success of marketing campaigns.
6. Academics and researchers can employ text analytics for various purposes like content analysis, sentiment analysis, or topic modeling in fields like social sciences, linguistics, or economics.
7. Legal professionals can use text analytics to review and analyze large volumes of legal documents, contracts, or court cases more efficiently.

Overall, anyone dealing with text-based data and aiming to extract insights, understand patterns, or make data-driven decisions can benefit from text analytics as a tool.

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