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Selecting The Correct Programming Language To Develop An AI Chatbot

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Online shopping does not follow one path only. The same as there are so many channels to list your products, there are multiple ways of making an online purchase—email, apps, and social media. Customers these days, as well as software developers, recognize how useful the bot tech is, and are ready to incorporate it into their web shopping. 

A chatbot is a program on a computer, which mimics conversations with humans for the completion of some service. For eCommerce companies, chatbots are designed typically to: 

  • Complete the purchase of buyers
  • Supply customer support
  • Offer product recommendations to buyers

So, which chatbot software development programming language should you build your bot with? Whatever language you choose, keep in mind that it’s as important as the human language that it understands. Bots are driving human interaction digitization, and definitely would turn into an unavoidable part of the regular daily existence of people everywhere. 

Let’s take a look at one particular programming language to develop AI chatbots.

Why Python?

Thanks to Python’s versatility, it is essentially the Swiss Army Knife of coding. Furthermore, it’s also one of the easier languages for a novice to choose with its consistent language and syntax, which mimics humans. This means that when it was first released, it was applied to more diverse instances compared to other programming languages like Ruby, which was restricted to designing and developing websites. 

Python grew in scientific computing, encouraging the building of a huge array of open-source libraries. When it comes to the NLP or the Natural Language Processing, the grandfather of integration was written in Python. Apache OpenNLP and Stanford NLP offer a compelling alternative for users of Java since both could amply support the development of chatbot either via tooling or could be used explicitly when calls are made through APIs. 

The faster you could code and get a chatbot running, the more reward that you get for the time spent. Python has a lot of benefits that others don’t have, making it ideal for chatbot development. The built-in workflow of the language enables troubleshooting programs while developing code. Python has become an efficient and reliable programming language due to its edit, test, and debug cycle. 

Why Learn Python?

  1. NLP: It could be a challenge to learn any programming language. The coding language of Python however is intuitive. Moreover, this opened the world to developers wanting to build simple but functional chatbots. 
  2. HTTP requests: The request module of the language enables developers to gather content seamlessly from a URL. The process is extremely important for building a conversational and realistic bot. Other coding programs require long codes for accomplishing content-grab requests of the same. 
  3. Coding that’s transferable: Some coding languages work best in specific industries. In Python, however, coding for any industry vertical is possible. You could not only develop bots, but you could also open career doors with your skills in the language, which comes in handy if you aim for that next job or next great project. 

 

On Machine Learning

In a survey recently of over 2,000 data scientists and machine learning software developers, over 57 percent used Python, while 33 percent made it a priority for development. The same as NLP, Python boasts of a huge range of open-source chatbot libraries, which include TensorFlow and scikit-learn. Scikit-learn is one of the most advanced around, with each Python machine learning algorithm. 

On the one hand, TensorFlow is more low-level. This versatility makes the Python language truly shine. Other languages have specific capabilities in terms of machine learning. Java has several libraries, such as its ML packages like Weka. While this is great for more simple analyses and small sets of data, the libraries of Python are considerably more practical. 

 

Where Python Wrestles

The biggest drawback of the language was in its documentation. Looking for answers within the language is the same as finding a certain passage in a book you’ve never read before. The language significantly lacks simple and useful examples. 

Clarity, being very important is another concern when creating a bot since even the least ambiguity in one of the steps could make it fail. If the main concern is speed, Java and C++ could offer more than Python. Nevertheless, the question would be if the time of executing code matters. 

The end-user experience is more important, and it’s self-defeating to choose a quicker yet more limited language for building chatbots. It, therefore,  makes no sense to sacrifice the development scope and time for a bot that functions a few milliseconds much quicker. 

NLP Implemented with Python

In its most basic form, sentiment analysis involves working out if the user is having a great experience or otherwise. If chatbots could recognize this, it would know when to pass a conversation to a human agent, which opening line best works, and what products users are more excited about. Check out this example:

“Great, my card isn’t working.”

It denotes a negative sentiment, but bots could find it hard to detect due to the word ‘great’. So, how should you equip the bot with strong sentiment analysis? You could train a bot using NLTK to recognize sentiment through first checking a set of data that is annotated manually. 

You could accomplish this through three lists, including positive comments, negative comments, and a test list that contains a mix. It requires extracting the most relevant words in every sentence, and rank them based on how often they appear within the data. Consider removing any words with less than three letters to be able to do this. 

A feature extractor could be used to create the remaining relevant words dictionary to build the finished training set that’s passed on to a classifier. Although it’s factually right to argue that ‘language is only a tool’, to equip a bot with AI, Python and its wider array of off-the-shelf algorithms and libraries mean it’s a more straightforward choice compared to other programming languages. 

 

Conclusion

Python, due to its open-source library and extreme versatility, could be considered a do-it-all coding language. With natural language and consistent syntax, it has made a name for itself as among the easiest coding languages for newbies to learn. 

Also, beginners who are wondering which language is worth learning and use to provide a voice to a chatbot, checking Python out is a great start. 

 

Author bio

I’m Eric Jones is a Content Strategist at TatvaSoft Australia, which is a Software development company in Melbourne. He would like to share ideas on technological trends, with  5+ years of experience He has been exploring the area of technology to produce interactive content on various subjects, including mobile apps, software development, and design, tech, etc.

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