Natural Language Processing (NLP) is a subfield of Artificial Intelligence which focusses on providing computers with the ability to understand, interpret and replicate human language.
Linguists have traditionally utilized qualitative methods and human interpreters to interpret the meaning of text. Whilst this approach is effective, it has some drawbacks, most notably the potential for unconscious bias to affect individuals’ judgement.
It has been determined that adult humans can only read between 200-250 words per minute, with college graduates tending to read at an average of 300 words per minute. This is an issue of importance to us.
The average novel typically consists of 90,000 to 100,000 words, which would take the average person approximately 70 hours to read. While this may seem like a significant amount of language, it is actually a relatively small proportion of the total amount of language created daily on social media.
It is estimated that around 500 million tweets are sent daily on Twitter, a social media platform limited to 280 characters per message. This equates to an approximate total of 100,000 volumes, assuming an average tweet length of 20 words. This is just one example of the many social networking sites now available.
Amassing Massive Amounts of Data
For researchers who are interested in social media, the sheer amount of data available can be overwhelming. Manual collection and analysis of this data is not a viable option. Thus, it is necessary to identify the most appropriate approach to address this issue.
Data collection via automated programming is made possible due to the Application Programming Interfaces (APIs) offered by most social media platforms. Web scraping has been a viable option since the inception of the internet and does not necessitate the use of an API.
Web scraping can be defined as the process of retrieving and extracting data from websites, either manually or automatically. Manual web scraping is more prevalent than automatic scraping.
It is uncertain as to the legality of web scraping. One of the more prominent cases of a major corporation challenging the practice is Facebook’s dispute with Power Ventures Inc. Power Ventures had constructed a platform allowing users to aggregate personal data from various sources, including LinkedIn, Twitter, Myspace and AOL.
Monitoring multiple Application Programming Interfaces (APIs) and being aware of relevant legislation are two of the primary challenges when managing social media. As an example, web scraping is allowed in Australia as long as email addresses are not collected.
The complexity of developer account and API levels can add an extra layer of complexity. Many services offer free tiers; however, these often have restrictions such as a limit to the size of queries or the number of records that can be accessed each month.
Twitter’s Search API sandbox allows up to 25,000 tweets per month, while a premium account permits up to 5,000,000. The sandbox is more suitable for pilot projects or proof of concept, while the premium account is designed for larger-scale projects.
Therefore, those that are interested in information collection via Social Media should:
- Get up to speed on the rules of data collection
- Learn the ins and outs of each platform’s API and developer accounts.
- Determine the possible cost by analyzing the scale of their project.
It’s all about knowing your target demographic.
It is generally accepted that people are drawn to those with similar interests and values. This is especially true when it comes to sharing experiences and passions. Social networking sites have become increasingly popular, and so the culture of the online communities is becoming more diverse as the user base expands.
NLP is effective in deciphering syntax, yet it has difficulty interpreting semiotics and pragmatics. This implies that computers are able to comprehend text and even create grammatically correct sentences. However, they struggle to understand nuances in the use of words and changes in language due to contextual factors.
AI still has a long way to go before it can identify satire and irony in text. While there is a limited quantity of sarcastic data, there are some interesting approaches that can be used to work with what is available.
It is essential to take into consideration cultural differences when developing machine learning models to analyze language on social media platforms. For instance, Twitter is frequently seen as one of the most hostile environments online, alongside Facebook.
It is reasonable to expect that the extent of any disagreements encountered will depend on the means of communication used. Furthermore, these distinctions are vital to be aware of.
Market researchers must ascertain which social media platform is preferred by their target demographic. Investigating trends on channels that provide limited useful data is an inefficient use of both time and resources.
Effortless Expression
The unprecedented rise of social media platforms such as Instagram and TikTok has presented Natural Language Processing (NLP) with a new challenge. Our system must be adapted to cater for the widespread usage of user-generated video and image content.
In the coming years, facial and vocal recognition technology will be transformative, as more and more creatives express themselves through video. Traditional methods of emotion analysis have often been inadequate for capturing the sentiment of the spoken word, which presents a unique challenge, as well as a potential opportunity.
It is too early to make a definitive prediction, however, if the key players in the internet industry continue to strive for a “metaverse,” it is likely that social media will progress to become more like MMORPGs such as Club Penguin or Second Life. This would create a virtual space where people could communicate with each other by taking advantage of their in-game microphones and virtual reality headsets.
It is too early to tell whether Meta will provide access to conversations for academics, however, historical evidence suggests that this is unlikely. We are still sometimes away from the development of the Metaverse, so it is impossible to be sure of the outcome at this stage.
Computational Linguistics and Data Science
Recent advancements in Natural Language Processing algorithms enabled by faster and more capable computing power have created a revolution in the field. Nevertheless, NLP is only one tool among many that data scientists need to employ to maximize the potential of this technology. Thus, the ability to collect data, an understanding of the social context, and a dose of instinct are all essential to achieve successful outcomes.
It is an exhilarating time to be part of Natural Language Processing, and we can be sure that the field will continue to grow in the coming years, offering increasingly advanced techniques for interpreting human language.
