Sentiment analysis – or else known as opinion mining .In short, it is the process of defining the expressive tone behind a series of words, in the habit of gain an understanding of the attitudes, opinions and emotions expressed within an online mention. Sentiment analysis is extremely useful in social media monitoring as it allows us to gain an overview of the wider public opinion behind certain topics. The applications of sentiment analysis are broad and powerful. The ability to extract visions from social data is a practice that is being broadly adopted by organizations through the world.
Shifts in sentiment on social media have been revealed to correlate with shifts in the stock market. The ability to swiftly understand consumer assertiveness and react accordingly but that is not to say that sentiment analysis is a faultless science at all. The human language is intricate. Teaching a machine to analyze the numerous grammatical nuances, cultural dissimilarities, slang and misspellings that occur in online mentions is a difficult process. Teaching a machine to understand how context can affect tone is even more challenging. Humans are impartially instinctive when it comes to interpreting the tone of a piece of writing.
Contemplate the following sentence: “My flight‘s been delayed. Brilliant!”
Most humans would be able to swiftly interpret that the person was being cynical. We distinguish that for most people having a delayed flight is not a good experience (unless there‘s a free bar as recompense involved). By applying this circumstantial understanding to the sentence, we can easily identify the sentiment as negative. Without contextual understanding, a machine considering at the sentence above might see the word ”brilliant” and groups it as positive.
That‘s not only dissimilar to how a linguist proficient would explain a machine how to conduct basic sentiment analysis. As language evolves, the dictionary that machines use to comprehend sentiment will continue to expand. With the use of social media, language is evolving faster than ever before. 140 character limits, the need to be succinct and other prevailing memes have transformed the ways we talk to each other online. This of course brings with it many challenges.
We proceeds all the words and phrases that imply positive or negative emotion and apply rules that contemplate how context might affect the tone of the content. With judgment crafted rules help our software discern the first sentence below is positive and the second is negative.
The above examples show how sentiment analysis has its limitations and is not to be used as a 100% accurate marker. As with any automated process, it is prone to error and often needs a human eye to lookout over it. Sentiment Extraction (SE) deals with the retrieval of the opinion or mood conveyed in a block of unstructured text in relation to the domain of the document being analyzed.
The extraction is accomplished in steps:
At the lowest level, we have rating words such as adjectives or adverbs that play a key role in determining polarity of a sentence. Examples of positive rating words include “good”, “awesome”, “excellent” and soon. At the other end of the spectrum such as ―”bad”, “poor”, “abomination” and so on.
1) At the following level, we have contextual polarity of the rating word that takes into account local modifiers that precede or succeed the rating word.
2) At the utmost level, these rating words are attached to some entity, typically the subject of some discussion.
As a complete example, in the sentence ―”The gouda was abysmal”, the entity is ―”gouda” and a negative sentiment is being expressed about this entity.