Text analysis techniques incorporate the use of different tools. Such tools include data mining, statistics, machine learning, information retrieval, and computational linguistics. It is a multidisciplinary field that may be challenging if you do not have the right knowledge and software to help you with the process.
You must follow specific steps when analyzing text. We can summarize them as:-
– The gathering of data from multiple sources. Such sources include emails, customer feedback from surveys, blogs, plain text, product reviews, and web pages, to name a few.
– Data pre-processing and cleaning to identify and remove any anomalies. The process allows for the extraction and retention of valuable information that you may not be seeing within your data. There are specific applications and tools you need to complete this step.
– Structuring of data by converting the information you need from the unstructured data.
– Pattern analysis through classifications or categorizations
Storage of the data you have structured in secure databases
Let’s explore the steps above in a little more detail while highlighting the text analysis techniques.
1. Extraction of Information
When you receive a chunk of text or data, you must find meaningful information within. The extraction of information requires you to look at the different attributes and entities and their relationship.
Once you access the information and extract it, you must pass it through relevant checks to determine its relevance and efficacy.
2. Retrieval of Information
Information retrieval looks at patterns using specific parameters such as phrases or set words. The algorithms will help monitor and track end-user behavior to come up with the relevant information.
Search engines like Yahoo and Google use information retrieval to give you the right results every time you type in a search query.
Categorization takes everyday language and assigns it to topics depending on their content. Think about it more like natural language processing (NLP) or supervised learning, which gathers, processes, and analyzes text documents.
It does it by revealing indexes or topics within each page, sentence, or sub sentence. You may, for example, find its use in hierarchical definitions, personalized commercials, spam filtering, amongst others.
Clustering will put information into clusters or subgroups by identifying intrinsic structures in the data. The process comes with its own challenges because you do not have any prior information on the unlabeled textual data.
It makes it difficult to form any meaningful clusters. However, it is a critical step when processing your data, especially if you want to use other algorithms to analyze your text.
5. Text Summarization
The text you finally generate from the text analysis must make sense to the end-user. As the name suggests, summarization brings all the information together into digestible chunks.
It allows for the presentation of the findings without changing the original document’s intent or meaning. You will find a lot of text categorization techniques in summarization, such as neural networks, decision trees, swarm intelligence, and regression models.
6. Sentiment Analysis
Sentiment analysis gives you more in-depth information about what your customers feel about your brand or product. It looks at emotions, feelings, or emotional polarity such as positive, negative, or neutral.