The subfield of Artificial Intelligence that prioritises equipping computers with the capacity to comprehend, decipher and imitate human language is known as Natural Language Processing (NLP).
Qualitative methodologies and human analysts have been conventionally employed by linguists to construe text meaning. Although this technique is efficient, it has some limitations, specifically the possibility of unconscious partiality to influence the perception of individuals.
Research has shown that adult humans can only peruse 200-250 words per minute, with college graduates having an average reading speed of 300 words per minute. This is of considerable significance to us.
Typically, the average novel comprises 90,000 to 100,000 words, which necessitates approximately 70 hours for an average person to read. Despite appearing to be a substantial quantity of language, it constitutes a comparatively minute fraction of the overall language produced daily on social media.
Twitter, a social media platform with a 280-character message limit, is estimated to produce about 500 million messages each day, equivalent to roughly 100,000 books assuming a 20-word average tweet length. This is merely one instance of the numerous social networking sites accessible today.
Accumulating Tremendous Quantities of Data
The vast quantity of data accessible on social media can be quite daunting for researchers. Manual collection and examination of such data is not a practical alternative. Hence, a suitable method must be selected to address this concern.
Automated programming data collection is facilitated through the Application Programming Interfaces (APIs) provided by the majority of social media platforms. Web scraping has been a workable alternative since the internet’s inception, and it does not mandate the use of an API.
Retrieving and extracting data from websites, either manually or automatically can be referred to as web scraping. While automatic web scraping exists, manual web scraping is more widespread.
The legality of web scraping is uncertain. Facebook’s discord with Power Ventures Inc. is one of the most significant instances of a large corporation objecting to the practice. Power Ventures created a platform that allowed users to combine personal data from various sources, including LinkedIn, Twitter, Myspace and AOL.
Overseeing multiple Application Programming Interfaces (APIs) and being cognizant of pertinent laws are the principal difficulties in managing social media. For instance, in Australia, web scraping is permissible provided email addresses are not collected.
The intricacy of developer accounts and API tiers can add an additional layer of complexity. Although various services provide free levels, they frequently come with limitations, such as a cap on the size of seraches or the amount of data that can be accessed per month.
Twitter’s Search API sandbox permits 25,000 tweets per month, while a premium account allows up to 5,000,000. The sandbox is better suited for pilot projects or proof of concept, whereas the premium account is intended for more extensive projects.
Hence, individuals interested in collecting data from Social Media should:
- Familiarise themselves with the regulations governing data collection
- Acquire in-depth knowledge of each platform’s API and developer accounts.
- Calculate the potential expenses by assessing the scope of their project.
Understanding your target audience is key.
It is widely acknowledged that individuals tend to gravitate toward those who share similar interests and values, particularly when it comes to sharing experiences and enthusiasms. Social networking sites’ popularity is growing, resulting in a more varied online community culture as the user base expands.
NLP is proficient in understanding syntax, but it struggles with interpreting semiotics and pragmatics. This means that computers can comprehend text and even construct grammatically sound sentences. However, they face difficulties in understanding the nuances of word usage and language changes due to contextual factors.
AI has a considerable distance to cover before it can accurately detect satire and irony in text. Although there is only a restricted amount of sarcastic data, there are some fascinating techniques that can be utilised to work with the data that is available.
When creating machine learning models to analyse language on social media platforms, it is crucial to consider cultural differences. For example, Twitter is often regarded as one of the least welcoming environments online, alongside Facebook.
It is logical to assume that the level of disagreements faced will vary depending on the mode of communication employed. Additionally, being aware of these distinctions is crucial.
Market researchers need to identify the social media platform favoured by their target audience. Examining trends on channels that provide little helpful data is an ineffective utilisation of both time and resources.
Seamless Articulation
The surge in popularity of social media platforms such as Instagram and TikTok has introduced Natural Language Processing (NLP) to a new obstacle. Our system needs to be adjusted to handle the prevalent use of user-generated video and image content.
Facial and vocal recognition technology will have a profound impact in the upcoming years, as a greater number of creatives convey their ideas through video. Conventional techniques for analysing emotions have frequently been insufficient in capturing the tone of spoken words, which presents a distinct challenge, as well as a probable opportunity.
As of now, it is too early for a decisive prognosis. However, if the pivotal figures in the internet sector persist in pursuing a “metaverse,” it is probable that social media will evolve to resemble MMORPGs like Club Penguin or Second Life. This would result in a virtual domain in which individuals could interact with one another by leveraging their in-game microphones and virtual reality headsets.
It is currently too soon to determine if Meta will grant academics access to conversations. However, historical data indicates that this is improbable. As we are still far from the creation of the Metaverse, it is impossible to be certain of what the outcome will be at this point.
Computational Linguistics and Data Analysis
Recent advancements in Natural Language Processing algorithms enabled by better and more capable computing power have sparked a revolution in the area. However, NLP is just one of the many tools that data scientists must utilise to fully exploit the potential of this technology. Therefore, collecting data, comprehending the social context, and an intuitive sense are all crucial elements to attain positive outcomes.
Being involved in Natural Language Processing is an exciting time, and it is assured that the domain will continue to expand in the upcoming years, providing more advanced methods for interpreting human language.
