Text analytics uses computational techniques to analyze unstructured text data and extract insights such as sentiment analysis or topic modeling.
At Phygital Insights, we have deep expertise and experience delivering text analytics services to multiple businesses. Our services aim to assist you in your decision-making capabilities by understanding customer sentiments and identifying emerging trends.
We assist you in optimizing your business performance with the following Text Analytics services:
Determine the attitude and emotion expressed in a text by analyzing words, phrases, and sentences.
Discover latent topics from the collection of documents using machine learning algorithms, identify patterns in text data, and arrange similar words into topics.
Discover and classify named entities in text into predefined categories such as names, locations, organizations, monetary values, etc.
Generate concise and coherent summaries of large volumes of text without affecting its originality and meaning.
Identify and extract important and relevant keywords and phrases from large texts to analyze and comprehend the underlying themes.
Analyze and Identify grammatical categories, such as nouns, verbs, adjectives, etc., for text classification, sentiment analysis, and language translation.
Identify the grammatical structure of sentences in the data set by finding the relationship between its words.
Identify and classify emotions in the written text to better understand customer feelings and fine-tune your products or services accordingly.
Improved decision-making through data-driven insights
Increased efficiency with automated text processing
Extracted actionable information from unstructured data
Enhanced customer experience through sentiment analysis
Identify emerging trends for proactive decision-making
Reduced costs with streamlined information management
Improved regulatory compliance through accurate reporting
Increased accuracy of text classification and clustering
We implement access controls to restrict data use, regularly audit access logs to detect potential security breaches, and comply with data privacy regulations.
Our high-quality text analytics solutions enable you to identify emerging trends and new opportunities and make early moves.
Our services analyze unstructured data from social media, surveys, and customer feedback to extract insights into customer sentiment, preferences, and behavior.
We deliver valuable insights into market trends and competitor behavior by analyzing text data from news articles, competitor websites, and social media.
We analyze texts and sentiments to help you make informed decisions about strategy, product development, and customer targeting.
We bank on automated processes to extract, classify, and analyze text data helping you save on time and costs.
We offer text analytics services to multiple industries. These industries include:
Banking and financial services
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Text analytics is converting unstructured text data into meaningful and useful information using techniques such as statistical analysis, text mining, natural language processing, and machine learning algorithms. It involves identifying, extracting, and analyzing patterns and insights from text data sources such as social media posts, customer reviews, emails, and other documents. Text analytics companies aim to derive actionable insights and support decision-making for applications such as sentiment analysis, customer experience management, and risk management.
Text analysis evaluates and extracts meaningful information from text data to discover patterns, trends, sentiments, topics, and other insights. It typically involves using natural language processing and machine learning techniques to analyze large amounts of text data. The outcome of text analysis is often used for various applications such as sentiment analysis, opinion mining, and topic modeling, among others.
Some common techniques in the text analytics market include sentiment analysis, keyword extraction, topic modeling, text classification, and named entity recognition.
Sentiment analysis is a subfield of text analytics that automatically identifies the sentiment expressed in text, such as positive, negative, or neutral.
Topic modeling is a text analytics tool that automatically identifies topics in a corpus of text documents. It is used to uncover latent structures in large text data and identify recurring themes or topics.
Text classification is the process of categorizing text data into predefined categories based on its content. It is used to organize and categorize large amounts of text data and make it more manageable and usable using the best text analysis tools.
Named entity recognition is a subfield of text analysis that involves identifying named entities in text, such as people, organizations, locations, and events. It is used to extract structured information from unstructured text data.
Working with the Phygital data engineering team has been a game-changer for our company. They helped us identify our data ingestion problems and implemented a centralized system to tackle them. We highly recommend their services.
Technical Product Manager
We needed support to help grow our retail business with data-driven recommendations. We found it in Phygital Insights’ Data Analytics Services. They studied our data, uncovered hidden patterns, and enabled us to anchor decisions on data-driven insights. Don’t look beyond Phygital for all your data needs.
Product Manager, US-based Retail Firm
We were struggling to increase our combined yield for all processes. After several futile efforts, we turned to Phygital. Their end-to-end analysis helped us identify hidden bottlenecks in the production line. We plugged them in to cut down on wastage and improve overall efficiency. Their service was indeed a game-changer.
Senior Operations Manager, US-based Manufacturing Company