Study of the Yahoo-Yahoo Hash-Tag Tweets Using Sentiment Analysis and Opinion Mining Algorithms
Abstract
:1. Introduction
- Collect tweets based on the Yahoo-yahoo hashtags using the Orange Twitter API.
- Pre-process and tokenize the tweets using a pre-trained tweet tokenizer.
- Conduct unsupervised lexicon-based sentiment analysis on the tweet corpus using the Liu Hu and VADER techniques, respectively.
- Carry out Topic modeling to detect abstract topics on corpus using Latent Dirichlet Allocation (LDA) and Latent Semantic Indexing (LSI) algorithms, respectively.
- Validate the topic modeling using Multidimensional Scaling (MDS) graph and Marginal Topic Probability (MTP).
2. Literature Review
3. Research Method
3.1. Data Collection
3.2. Data Pre-Processing
- Converting all characters in the corpus to lowercase;
- Remove all HTML tags from a string;
- Removing all text-based diacritics and accents;
- Removing URLs, articles, and punctuations;
- Filtering stop words, lexicon, Regular expressions.
3.3. Sentiment Analysis
Algorithm 1: Duplicate Detection and Sentiment Analysis Workflow. |
Input: {Corpus C; Tweet T (T1, T2, …Tn), Tweet contents: X = {x1, x2, …xn}, distance between rows, d distance threshold, dT = 0.5; distance metrics, m} Output: {Liu Hu: Sentiment Score; VADER: Sentiment Scores (Neg, Neutral, Positive, Score); Heat maps} Start: Procedure Step 1: For ∀ T ϵ C T ← {T1, T2, … Tm} T1 = {, , … }, T2 = {, , … }, ⠇ ⠇ ⠇ ⠇ Tn = {, , … } Step 2: Detect Duplicate Tweets T using Manhattan Distance (d) For i = 1: n \\ distance between rows Calculate: d = ( == ) && ( == ) && … ( == ) Linkage L= single d > =dT remove duplicate Step 3: Apply Sentiment Analysis Method (Liu Hu; VADER) Generate output end |
3.4. Ground Truth Generation for Sentiment Analysis
3.5. Topic Modelling
Algorithm 2: Workflow for the Topic Modelling. |
Input: {Corpus C; Tweet T (T1, T2, …Tn), Tweet contents: X (x1, x2, …xn), Authors} Output: {MDS: Marginal Topic Probability (MTP) of LDA topics, Word Cloud for LSI and LDA topics, Boxplots: MTP for LDA topics 1 to 6} Start: Step 1: Pre-process Text 1.1 Transformation {Lower case, remove accents, parse html, Remove all html tags from strings, and remove URLs} 1.2 Tokenization: Regexp (\w+) 1.3 Filtering: {Remove stopwords, Regexp (\. |,|:|;|!|\?|\ (|\ )|\||\+|’|”|‘|’|“|”|’|\’|…|\-|–|— |\$|&|\*|>|<|\/|\ [|\ ]), Document Frequency DF = (0.10–0.90)} Step 2: Topic Modelling Methods Apply Latent Semantic Indexing (LSI) Apply Latent Dirichlet Allocation (LDA); Step 3: Plot Multidimensional Scaling (MDS) graph Generate outputs end |
4. Results and Discussion
4.1. Results of Pre-Processing and Tokenization
4.2. Results of Geolocation
4.3. Results of Duplicate Detection
4.4. Result of Sentiment Analysis
4.5. Results of Ground Truth Generation for Sentiment Analysis
4.6. Results of Topic Modelling
4.7. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
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Article Sources | Methods | Contributions | Research Domain |
---|---|---|---|
[26] | Machine learning based on logistic regression. | Result shows the proposed method could be effective and reliable for investigating the crime. | Homicide detection. |
[21] | ℓ1 regularization regression algorithm. | Proposed methods were useful to predict possible cyber-attacks. | Cyber-attack detection. |
[32] | SVM | Significant improvement in classification accuracy. | Detection Traffic Congestion. |
[33] | Ensemble method based on Linear SVM, Radial SVM, Polynomial SVM, R.F., and N.B. | The proposed method gave a reliable capacity to predict relevancy with an improvement in accuracy of more than 6%. | Relevance Detection. |
[34] | Stochastic gradient descent (SGD) approach to training of SVM classifier. | Improved prediction accuracy for the detection of social tension topics in Russia. | Social tension detection. |
[35] | CyberEM model based on pattern clustering and an NMF-based (non-negative matrix factorization) event aggregation algorithm. | The proposed model was able to discover cybersecurity events and update event aggregation online. | Event detection. |
[36] | R.F. algorithm. | Developed a low-cost interpretative model. | Identity deception. |
[28] | SMOTE approach on supervised ML (N.B., SVM, R.F., and KNN). | Develop a cost-sensitive model. | Cyberbullying detection. |
[37] | K-means clustering algorithm and Random Forest algorithm. | The proposed methods were able to show significant prediction power in detecting cyberbullying. | Cyberbullying behavior |
[38] | Ensemble machine Classification and Statistical Modelling. | Classification results showed very high levels of performance at reducing false positives and produced promising results with respect to false negatives. | Cyber Hate Speech |
S/N | Words | Frequency/Weight |
---|---|---|
1 | yahoo | 9555 |
2 | pastor | 745 |
3 | forget | 668 |
4 | adeboye | 628 |
5 | arrested | 511 |
6 | friend | 499 |
7 | bad | 498 |
8 | status | 488 |
9 | ps4 | 488 |
10 | bag | 488 |
11 | 487 | |
12 | Updated | 486 |
S/N | Cluster | No. of Retweet | Content |
---|---|---|---|
1 | C91 | 484 | My friend just updated on his status that policemen arrested him at Ojuelegba for having a Ps4 in his bag, claiming that he was a yahoo boy. |
2 | C85 | 351 | I’m not in support of Yahoo yahoo; it’s really bad but let’s face the fact that it’s yahoo yahoo that’s still lowering poverty |
3 | C110 | 173 | Forget yahoo yahoo for a moment and be as smooth as this kitty. |
4 | C80 | 172 | This is one of the funniest video you will see on Twitter today. |
5 | C62 | 162 | EFCC Arrests Landlord for housing Yahoo boys. This comprises of more than one form of a tweet (e.g., EFCC, Bad Governance, Landlord, yahoo yahoo government etc.) |
6 | C87 | 150 | Yahoo yahoo is like opting for the easy way out, limiting your potentials, why not channel that same energy towards something worthwhile and good. |
7 | C123 | 142 | Grow your Twitter audience now. As we can’t do fraud, we can’t do Yahoo yahoo, we can’t steal, and we can’t be lazy |
8 | C63 | 141 | This Administration is a scam. EFCC is yahoo yahoo. Every sector of this nation is in Coma. (This talks about the resignation of President Buhari, Fulani Herdsmen, Budget of $12m, EFCC and Yahoo boys) |
9 | C79 | 127 | Ladies who collect T-Fare from a man and end up not visiting him without a cogent reason are the real Yahoo Yahoo. |
10 | C88 | 127 | Yahoo yahoo—they will brainwash you and make you give them your money. Fraud—you will give them your money on your own free (This emphasis on difference between yahoo yahoo and fraud. Also, it contains tweets on Rochas, linkage with Government and that they are better than politicians) |
11 | C103 | 123 | I’m not even going to judge anybody doing yahoo yahoo. |
12 | C126 | 122 | Forget, NYSC, Yahoo yahoo, Mertens, Pablo and pastor Adeboye, Twitter people don’t have respect. |
13 | C89 | 103 | I don’t know why Yahoo Yahoo is trending, but you all should take your time and admire this flawless makeup |
14 | C100 | 103 | The greatest, easiest and most legitimate form of yahoo yahoo in Nigeria is politics |
15 | C107 | 99 | D.O. girls also do yahoo yahoo? Or is it only the boys? |
16 | C104 | 91 | Problems caused by yahoo yahoo scammers government (This is on corruption, bribery, fraud, yahoo-yahoo and scammers) |
17 | C142 | 91 | Legit work that pays. Say No to Yahoo Yahoo. |
18 | C102 | 89 | Yahoo yahoo is bad, instead just be a pastor, imam or a politician. |
19 | C109 | 81 | Between 2017 and January 2020, F.G. has repatriated $1.89Billion of Abacha Loot. |
20 | C96 | 79 | To SARS you are doing yahoo yahoo o. they should just arrest themselves. |
21 | C71 | 79 | Someone said Yahoo Yahoo is now a course in his University. |
LDA Topic Keywords | ||||||
Topic 1 | Topic 2 | Topic 3 | Topic 4 | Topic 5 | Topic 6 | |
1 | Yahoo | yahoo | trending | rt | money | yahoo |
2 | rt | rt | 🤣 | geng | 😭 | rt |
3 | go | bad | make | 😅 | thiem | pastor |
4 | like | arrested | merlin | set | since | retweet |
5 | said | updated | nadal | 🙂 | #whatwentwrong | forget |
6 | fraud | status | know | order | give | 😂 |
7 | end | ps4 | time | @mazedgreat | get | adeboye |
8 | reason | bag | someone | everyone | daddy | 💦 |
9 | man | friend | take | sars | need | @jfriks |
10 | real | ojuelegba | ur | 10 | saying | = |
LSI Topic keywords | ||||||
Topic 1 | Topic 2 | Topic 3 | Topic 4 | Topic 5 | Topic 6 | |
1 | yahoo | rt | 😂 | 😂 | fg | like |
2 | rt | yahoo | = | 2020 | even | |
3 | 😂 | = | pastor | pastor | january | lot |
4 | bad | arrested | adeboye | nysc | abacha | going |
5 | pastor | bag | arrested | adeboye | 3 | terrifying |
6 | really | friend | friend | rt | loot | judge |
7 | let | ps4 | bag | followers | 2017 | anybody |
8 | support | policeman | ps4 | forget | repatriated | racism |
9 | still | status | policemen | 200 | n681billion | homosexuality |
10 | @adehdaboy | claiming | updated | 100 | yrs | stuffs |
LDA | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
S/N | Topic 1 | Topic 2 | Topic 3 | Topic 4 | Topic 5 | Topic 6 | ||||||
Word | Weight | Word | Weight | Word | Weight | Word | Weight | Word | Weight | Word | Weight | |
1 | yahoo | 0.17 | nadal | 0.028 | yahoo | 0.101 | rt | 0.095 | rt | 0.04 | yahoo | 0.234 |
2 | rt | 0.066 | money | 0.028 | rt | 0.062 | yahoo | 0.092 | #whatwentwrong | 0.018 | rt | 0.075 |
3 | 🤣 | 0.028 | geng | 0.026 | pastor | 0.039 | arrested | 0.042 | order | 0.013 | bad | 0.026 |
4 | end | 0.022 | thiem | 0.021 | forget | 0.037 | updated | 0.041 | 😅 | 0.011 | really | 0.023 |
5 | said | 0.022 | around | 0.02 | 😂 | 0.028 | status | 0.041 | 10 | 0.009 | still | 0.022 |
6 | man | 0.02 | crush | 0.018 | adeboye | 0.027 | ps4 | 0.041 | chop | 0.009 | support | 0.022 |
7 | someone | 0.019 | pls | 0.017 | 💦 | 0.026 | bag | 0.041 | name | 0.009 | @adehdaboy | 0.021 |
8 | real | 0.019 | since | 0.016 | @jfriks | 0.023 | friend | 0.041 | available | 0.009 | let | 0.021 |
9 | 😭 | 0.018 | efcc | 0.016 | equal to | 0.023 | claiming | 0.04 | 25 | 0.009 | fact | 0.021 |
10 | country | 0.018 | self | 0.016 | followers | 0.022 | ojuelegba | 0.04 | upandan | 0.008 | face | 0.021 |
Topic 1 | Topic 2 | Topic 3 | Topic 4 | Topic 5 | Topic 6 | |||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
S/N | Word | Weight | Word | Weight | Word | Weight | Word | Weight | Word | Weight | Word | Weight |
1 | yahoo | 0.087 | rt | 0.511 | 😂 | 0.403 | 😂 | −0.382 | fg | 0.226 | like | −0.294 |
2 | rt | 0.362 | yahoo | −0.232 | 0.218 | 0.209 | 2020 | 0.224 | even | −0.258 | ||
3 | 😂 | 0.092 | 0.200 | pastor | 0.195 | pastor | −0.180 | january | 0.224 | lot | −0.248 | |
4 | bad | 0.067 | arrested | 0.194 | adeboye | 0.190 | nysc | −0.173 | abacha | 0.224 | going | −0.246 |
5 | pastor | 0.062 | bag | 0.193 | arrested | −0.185 | adeboye | −0.171 | 3 | 0.224 | terrifying | −0.246 |
6 | really | 0.060 | friend | 0.193 | friend | −0.185 | rt | 0.165 | loot | 0.224 | judge | −0.245 |
7 | let | 0.058 | ps4 | 0.193 | bag | −0.184 | followers | 0.164 | 2017 | 0.224 | anybody | −0.245 |
8 | support | 0.058 | policeman | 0.193 | ps4 | −0.184 | forget | −0.160 | repatriated | 0.224 | racism | −0.245 |
9 | still | 0.058 | status | 0.193 | policeman | −0.184 | 200 | 0.156 | 1.89 | 0.224 | stuffs | −0.245 |
10 | @adehdaboy | 0.057 | ojuelegba | 0.193 | updated | −0.184 | 100 | 0.156 | n681billion | 0.224 | homosexuality | −0.245 |
S/N | Topic 1 | Topic 2 | Topic 3 | Topic 4 | Topic 5 | Topic 6 |
---|---|---|---|---|---|---|
1 | 😂 | money | Pastor | arrested | order | bad |
2 | end | geng | Retweet | updated | whatwentwrong | really |
3 | someone | nadal | forget | status | 10 | still |
4 | real | thiem | 😂 | ps4 | chop | support |
5 | 😭 | around | adeboye | bag | name | @adehdaboy |
6 | without | crush | 💦 | friend | available | let |
7 | ladies | since | @jfriks | claiming | 25 | fact |
8 | collects | efcc | = | ojuelegba | shout | face |
9 | t-fare | self | followers | policemen | upandan | lowering |
10 | @biyitheplug | laugh | 100 | @aproko_doctor | funds | pove |
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Abayomi-Alli, A.; Abayomi-Alli, O.; Misra, S.; Fernandez-Sanz, L. Study of the Yahoo-Yahoo Hash-Tag Tweets Using Sentiment Analysis and Opinion Mining Algorithms. Information 2022, 13, 152. https://doi.org/10.3390/info13030152
Abayomi-Alli A, Abayomi-Alli O, Misra S, Fernandez-Sanz L. Study of the Yahoo-Yahoo Hash-Tag Tweets Using Sentiment Analysis and Opinion Mining Algorithms. Information. 2022; 13(3):152. https://doi.org/10.3390/info13030152
Chicago/Turabian StyleAbayomi-Alli, Adebayo, Olusola Abayomi-Alli, Sanjay Misra, and Luis Fernandez-Sanz. 2022. "Study of the Yahoo-Yahoo Hash-Tag Tweets Using Sentiment Analysis and Opinion Mining Algorithms" Information 13, no. 3: 152. https://doi.org/10.3390/info13030152