How to choose the best text analytics solution and a comparative analysis of various NLP tools

Introduction

Great advances in artificial intelligence and data science have led to great improvements in various text analytics tools and solutions. As such, this has led to direct or indirect improvements in the development and testing of text analytics solution. In the present AI era, natural language processing has culminated into the perfect interpretation of the cognitive components of human language. The improvements in machine learning methodologies have led to greater improvement in text analytics solutions and tools.

Highlighting the features of various platforms

The first important feature of text analytics platforms is the extraction of the value of topics. This is done by tagging the text based on various themes. The next important feature is the extraction of important entities in the text. This includes the identification of different details like addresses and emails in a randomly segregated piece of information. An important feature is the detection of language. This is an automated feature that gives the syntax of the language that the writer has used. After this comes the feature of analysis of emotions. This gives us the context in which the article was written and gives us the mood of the text. Another feature is related to emotion, and this is called sentiment analysis. There are numerous applications arising out of sentiment analysis. The prime function of this analysis is that it enables us to classify the text in a positive or negative context.

Methodologies for choosing the best text analytics solution

The first important methodology that we apply for the selection of text analytics solution is the process of planning workflows. Text analytics demands the import of data from various sources and applications. After this, the processed data set would need to be exported to other applications. Here comes the function of the application programming interface which determines the smooth integration of our text analytics methodology into another application. Application of the integration tools enables us to smoothen our workflows and processes. After this comes the process of deploying our preferences. We know that cloud environments are best suited to certain types of text analytics platforms. There are other applications that can run over an on-premise ecosystem. So, we need to select a set of deployment facilities that matches our security and compliance requirements. The final task is the conduction of pricing analysis. Various types of text analytics vendors list their pricing strategies according to their own requirements. As these pricing strategies are not customized according to the needs of the customer, they can be relatively complicated. This demands that a lot of groundwork is done so that pricing methodologies are appropriately selected.

Natural language processing solution: A comparative analysis

There are different types of natural language processing tools. One of the most important among them is Google Cloud’s natural language that is built on Auto ML machine learning methodology. This platform comes in three different modes. The first mode is customized for those who want to construct their own models and train data accordingly. The second mode is natural language API which is designed for those who want to add their own Natural Language Processing capabilities to the existing applications. The third mode enables real-time analytics of both structured and unstructured types of data. All deep modes support sentiment and emotion analysis and are driven by full-fledged multimedia support. One of the most important advantages of using this platform is its easy integration with Google Cloud services. The disadvantage of using this platform is its high level of difficulty for those who are new in the field.

Another important natural language processing solution is called Amazon comprehend. This platform is not only useful for syntax analysis but is also handy for language detection and topic modeling. It finds application in research-oriented fields like medical ontology. The advantage of using this platform is that it does not need the data science expertise that is required in other platforms. The disadvantage associated with this platform is its topic modeling feature that is difficult to use.

Concluding remarks

While selecting various types of natural language processing solution, the unique capabilities and customer service are important features that need to be considered. Easy and multiple deployment options along with the integration of other applications are what make text analytics solution stand out from the crowd.

Data Science | Big-Data | Product Engineering @ Algoscale