Latest thesis research topics and ideas in Sentiment Analysis

Latest thesis research topics and ideas in Sentiment Analysis

Sentiment Analysis is the interpretation of the sentiments behind data in the form of text. It is the computational analysis of emotions and opinions towards a subject. It involves working out whether a piece of text is a positive negative or neutral.

What is Sentiment Analysis?

Sentiment Analysis is a type of inspection of text using Machine Learning and NLP(Natural Language Processing) to inspect a person’s feelings and thoughts on a particular topic, situation, product or a company. It is also called opinion mining.

Consider the following piece of text “I am very proud of my country.” It is quite a positive statement irrespective of the tone in which it is said.

Another sentence “Your fitness level depends on the number of walking steps you take per day.” Now this sentence is simple and straightforward. It is neither positive nor negative, it is a neutral sentence.

The sentence “I hate the coffee, it is terrible.” Clearly it is a very negative sentence.

 

Why is Sentiment Analysis Important?

Sentiment Analysis helps analysing data of large companies to help them give a better understanding of their products, consumers and competitors. Customers express their views on Social Media platforms in the form of text generally through comment sections. Customers spend a huge amount of time browsing through public reviews of a product and the brand’s social media. Their decision of a purchase largely depends on what they consume in the form of text reviews even if they are not true. Therefore, sentiment analysis helps companies sight customer’s perception of them. The huge amount of data cannot be manually interpreted but can be done automatically with sentiment analysis.

Sentiment Analysis can be used for:

  1. Brand/ Social Media monitoring
  2. Surveying customer opinions
  3. Market research for competition and future trends

 

How does Sentiment Analysis work?

Sentiment Analysis can be performed with toolkits such as NLTK(Natural Language Toolkit), OpenCV, Pattern and SK Learn packages. NLTK provides a set of libraries that are used to process human language. It is used for sentiment analysis but it not only tells you whether a piece of text is positive, negative or neutral it also takes into account the intensity of the sentiment.

 

Sentiment Analysis is performed by a machine learning algorithm that takes input in the form of text files or excel sheets. The algorithm takes a string and then returns a rating of the sentiments. For example, the output for  ”I am very happy today” will also tell us upto what degree that this sentence is  positive or negative. It will also give  the intensity of my sentiment.

 

Example: Consider the following list of strings, the input:

{“stringsList”: [

“I love this movie”,

“I don’t want to have a coffee”]}

Output:

[{

“positive”: 0.512,

“negative”: 0,

“sentence”: “I love this movie”,

“neutral”: 0.488,

“compound”: 0.6369

},

{

“positive”: 0,

“negative”: 0.234,

“sentence”: “I dislike drinking coffee”,

“neutral”: 0.766,

“compound”: -0.0572

}]

 

For the first sentence “I love this movie” the Positive, Negative and Neutral scores represent the proportion of text that falls in these categories. This means that the sentence was rated as 51.2% Positive, 48.8% Neutral and 0% Negative. Since the scores are in the form of  percentage, all of the ratings add up to 1.

The output of the second sentence “I dislike drinking coffee” the Positive, Negative and Neutral scores are 0%, 23.4% and 76.6% respectively.

 

The compound score is computed by summing the valence scores of each word in the lexicon, adjusted according to the rules, and then normalized to be between -1 (most extreme negative) and +1 (most extreme positive)

  1. positive sentiment: compound score >= 0.05
  2. neutral sentiment: (compound score > -0.05) and (compound score < 0.05)
  3. negative sentiment: compound score <= -0.05

 

Challenges of Sentiment Analysis

  1. Sarcasm: Sentiment analysis determines whether a sentence is positive or negative depending upon the number of positive or negative words in that sentence. People often use positive words to describe negative feelings in a sarcastic way usually in social media comments. For example, “Well done, great!”. This sentence contains positive words but it is hard to tell if the tone was positive or negative.
  2. Negation Detection: Negation reverses the polarity of words. For example, “I dislike deforestation”, this sentence does not convey a negative message but the presence of “dis-” or “non-” can make such sentences seem negative.
  3. Multipolarity: In some sentences the end result of the analysis can be misleading. For example, “I like the battery capacity of the laptop but I do not like it’s display.” Some of the sentiment analysis models can assign this negative polarity to this sentence which is not entirely negative.

Deciding a research topic in sentimental analysis can be a daunting task. But don’t worry, if E2Matrix is here. We assist M.Tech and PhD students in deciding the topic for their thesis in context to sentimental analysis.

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Latest thesis research topics and ideas in Sentiment Analysis

The following are some latest thesis research topic in sentiment Analysis mostly used in M.tech and PhD thesis:

  1. Sentiment Infomation based Model For Chinese text Sentiment Analysis

  2. Sentiment analysis in a cross-media analysis framework

  3. Entity-Level Sentiment Analysis of Issue Comments

  4. Aspect Based Sentiment Analysis with Self-Attention and Gated Convolutional Networks

  5. Automatic Color Palette Design Using Color Image and Sentiment Analysis

  6. Aspect-Level Sentiment Analysis on E-Commerce Data

  7. FinSSLx: A Sentiment Analysis Model for the Financial Domain Using Text Simplification

  8. Sentiment Analysis Using Naive Bayes Algorithm Of The Data Crawler: Twitter

  9. Sentiment Analysis using Partial Textual Entailment

  10. Chinese Text Sentiment Analysis Based on Extended Sentiment Dictionary

 

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