File size: 25,025 Bytes
6fa4bc9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 |
{
"paper_id": "2020",
"header": {
"generated_with": "S2ORC 1.0.0",
"date_generated": "2023-01-19T07:29:21.580248Z"
},
"title": "Sentiment Analysis of English-Punjabi Code-Mixed Social Media Content",
"authors": [
{
"first": "Mukhtiar",
"middle": [],
"last": "Singh",
"suffix": "",
"affiliation": {},
"email": "mukhtiarrai73@gmail.com"
},
{
"first": "Vishal",
"middle": [],
"last": "Goyal",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "Punjabi University",
"location": {
"settlement": "Patiala"
}
},
"email": "vishal.pup@gmail.com"
},
{
"first": "Sahil",
"middle": [],
"last": "Raj",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "Punjabi University",
"location": {
"settlement": "Patiala"
}
},
"email": ""
}
],
"year": "",
"venue": null,
"identifiers": {},
"abstract": "Sentiment analysis is a field of study for analyzing people's emotions, such as Nice, Happy, \u0a26\u0a41 \u0a16\u0a40 (sad), changa (Good), etc. towards the entities and attributes expressed in written text. It noticed that, on microblogging websites (Facebook, YouTube, Twitter), most people used more than one language to express their emotions. The change of one language to another language within the same written text is called code-mixing. In this research, we gathered the English-Punjabi code-mixed corpus from micro-blogging websites. We have performed language identification of code-mix text, which includes Phonetic Typing, Abbreviation, Wordplay, Intentionally misspelled words and Slang words. Then we performed tokenization of English and Punjabi language words consisting of different spellings. Then we performed sentiment analysis based on the above text based on the lexicon approach. The dictionary created for English Punjabi code mixed consists of opinionated words. The opinionated words are then categorized into three categories i.e. positive words list, negative words list, and neutral words list. The rest of the words are being stored in an unsorted word list. By using the Ngram approach, a statistical technique is applied at sentence level sentiment polarity of the English-Punjabi codemixed dataset. Our results show an accuracy of 83% with an F-1 measure of 77%.",
"pdf_parse": {
"paper_id": "2020",
"_pdf_hash": "",
"abstract": [
{
"text": "Sentiment analysis is a field of study for analyzing people's emotions, such as Nice, Happy, \u0a26\u0a41 \u0a16\u0a40 (sad), changa (Good), etc. towards the entities and attributes expressed in written text. It noticed that, on microblogging websites (Facebook, YouTube, Twitter), most people used more than one language to express their emotions. The change of one language to another language within the same written text is called code-mixing. In this research, we gathered the English-Punjabi code-mixed corpus from micro-blogging websites. We have performed language identification of code-mix text, which includes Phonetic Typing, Abbreviation, Wordplay, Intentionally misspelled words and Slang words. Then we performed tokenization of English and Punjabi language words consisting of different spellings. Then we performed sentiment analysis based on the above text based on the lexicon approach. The dictionary created for English Punjabi code mixed consists of opinionated words. The opinionated words are then categorized into three categories i.e. positive words list, negative words list, and neutral words list. The rest of the words are being stored in an unsorted word list. By using the Ngram approach, a statistical technique is applied at sentence level sentiment polarity of the English-Punjabi codemixed dataset. Our results show an accuracy of 83% with an F-1 measure of 77%.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Abstract",
"sec_num": null
}
],
"body_text": [
{
"text": "In the last decade, the social media platform has been become the medium of communication such as Facebook, Twitter, LinkedIn, etc. (Yang, Chao et al., 2013; Fazil, Mohd et al., 2018) . On social media platform, everybody has a short time and the information to be analyzed is huge. Sentiment analysis helps us to whether the message or sentence follows positive or negative. Sentiment analysis is also known as opinion mining or opinion analysis. By using, the web forums there are so many sources to express their views to track and analyze opinions and attitudes about and product. In India, there are 22 official languages, and many more regions languages used for communication (W. Medhat et al., 2014) .",
"cite_spans": [
{
"start": 132,
"end": 157,
"text": "(Yang, Chao et al., 2013;",
"ref_id": "BIBREF8"
},
{
"start": 158,
"end": 183,
"text": "Fazil, Mohd et al., 2018)",
"ref_id": "BIBREF0"
},
{
"start": 683,
"end": 707,
"text": "(W. Medhat et al., 2014)",
"ref_id": "BIBREF7"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "There are lots of social media communication, which people use more than one languages to convey their opinion or sentiments (Kalpana et al., 2014; Sharma, S et al., 2015) . So, necessary to analyze the data to find appropriate sentiments. Ngrams are one of the most commonly used features (G. Rodrigues Barbosa et al., 2012; Kaur, A., & Gupta, V. 2014) . We used the n-gram approach up to fivegram and found that the results of fivegram are similar to trigram approach for English-Punjabi code mixed text. The type of ngram also depends on the type of domain used as some domains are more popular in phrases to express the sentiment. Accordingly, our tool gives the power to the users to choose one of two approaches: trigrams and fivegram.",
"cite_spans": [
{
"start": 125,
"end": 147,
"text": "(Kalpana et al., 2014;",
"ref_id": null
},
{
"start": 148,
"end": 171,
"text": "Sharma, S et al., 2015)",
"ref_id": "BIBREF6"
},
{
"start": 304,
"end": 325,
"text": "Barbosa et al., 2012;",
"ref_id": "BIBREF1"
},
{
"start": 326,
"end": 353,
"text": "Kaur, A., & Gupta, V. 2014)",
"ref_id": "BIBREF4"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "The main target of current research is sentiment analysis of English-Punjabi code mixed language at sentence level. The foremost task for developing the system is collection of Social Media Code-Mixed text using API twitter threads for Twitter, selecting some prolific users comments for Facebook as data and some student community prolific users chat for Whatsapp followed by cleaning of extracted data.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Methodology",
"sec_num": "2"
},
{
"text": "The dataset used in the current research consists of 10 Lakh sentences (tafter preprocessing) which have been tagged as en (English), pb (Punjabi), univ (Universal) and both (mixing of two languages inside a word), The features used are contextual features, capitalization features, special character features, character N Gram features and lexicon features.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Methodology",
"sec_num": "2"
},
{
"text": "In social media text people use creativity in spellings rather than traditional words. The deviation of text can be categorized as acronyms, slangs, misspellings, use of phonetic spellings etc. Contractions like hasn't-has not, ma'am-madam etc. which are handled by mapping. Plenty of common English words e.g. nyt -night, jan-January, gm-gud morning have changed their existence on social media. A dictionary of such out of vocabulary has been maintained in order to normalize them.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Methodology",
"sec_num": "2"
},
{
"text": "Generally, the performance of sentiment classification is evaluated by using four indexes: Accuracy with Precision plus Recall and F1-score. A random sample of 200 sentences is picked up for testing and firstly manual testing identified and then tested by a statistical tool. This comparison also discusses the challenges and solutions. We faced and devised on evaluating sentiment analysis. Table 1 represents an accuracy of 83 % with F1-score 77 % on the English-Punjabi code mixed data set the statistical approach.",
"cite_spans": [],
"ref_spans": [
{
"start": 392,
"end": 399,
"text": "Table 1",
"ref_id": "TABREF1"
}
],
"eq_spans": [],
"section": "Results",
"sec_num": "3"
},
{
"text": "The accuracy represents the rate at which the method predicts results correctly. The precision also called the positive predictive rate, calculates how close the measured values are to each other. A F-measures that combines precision and recall is the harmonic mean of precision and recall. This score takes both false positives and false negatives into account. In order to compute the accuracy of each technique, by calculating the intersections of the positive or negative proportion given by each technique. Table 1 presents the percentage of accuracy for fivegram approach and trigram approach. ",
"cite_spans": [],
"ref_spans": [
{
"start": 512,
"end": 601,
"text": "Table 1 presents the percentage of accuracy for fivegram approach and trigram approach.",
"ref_id": "TABREF1"
}
],
"eq_spans": [],
"section": "Results",
"sec_num": "3"
}
],
"back_matter": [],
"bib_entries": {
"BIBREF0": {
"ref_id": "b0",
"title": "A hybrid approah for detecting automated spammers in twitter",
"authors": [
{
"first": "Mohd",
"middle": [],
"last": "Fazil",
"suffix": ""
},
{
"first": "Muhammad",
"middle": [],
"last": "Abulaish",
"suffix": ""
}
],
"year": 2018,
"venue": "IEEE Transactions on Information Forensics and Security",
"volume": "13",
"issue": "",
"pages": "2707--2719",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Fazil, Mohd,and Muhammad Abulaish. A hybrid approah for detecting automated spammers in twitter. IEEE Transactions on Information Forensics and Security, vol. 13, pages 2707-2719, 2018.",
"links": null
},
"BIBREF1": {
"ref_id": "b1",
"title": "Characterizing the effectiveness of twitter hashtags to detect and track online population sentiment",
"authors": [
{
"first": "G",
"middle": [
"Rodrigues"
],
"last": "Barbosa",
"suffix": ""
},
{
"first": "I",
"middle": [],
"last": "Silva",
"suffix": ""
},
{
"first": "M",
"middle": [],
"last": "Zaki",
"suffix": ""
},
{
"first": "W",
"middle": [],
"last": "Meira",
"suffix": ""
},
{
"first": "R",
"middle": [],
"last": "Prates",
"suffix": ""
},
{
"first": "A",
"middle": [],
"last": "Veloso",
"suffix": ""
}
],
"year": 2012,
"venue": "Proceedings of the 2012 ACM annual conference extended abstracts on Human Factors in Computing Systems Extended Abstracts-CHIEA",
"volume": "1",
"issue": "",
"pages": "1186--1195",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "G. Rodrigues Barbosa, I. Silva, M. Zaki, W. Meira, R. Prates and A. Veloso, Characterizing the effectiveness of twitter hashtags to detect and track online population sentiment, Proceedings of the 2012 ACM annual conference extended abstracts on Human Factors in Computing Systems Extended Abstracts-CHIEA, vol.1, pages 1186- 1195, 2012.",
"links": null
},
"BIBREF2": {
"ref_id": "b2",
"title": "Data analysis using regression and multilevel/ hierarchical models",
"authors": [
{
"first": "A",
"middle": [],
"last": "Gelman",
"suffix": ""
},
{
"first": "J",
"middle": [],
"last": "Hill",
"suffix": ""
}
],
"year": 2007,
"venue": "",
"volume": "1",
"issue": "",
"pages": "1--6",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Gelman, A. & Hill, J. Data analysis using regression and multilevel/ hierarchical models, vol.1, pages 1- 6, 2007.",
"links": null
},
"BIBREF3": {
"ref_id": "b3",
"title": "A survey on data mining techniques in agriculture",
"authors": [
{
"first": "R",
"middle": [],
"last": "Kalpana",
"suffix": ""
},
{
"first": "N",
"middle": [],
"last": "Shanthi",
"suffix": ""
},
{
"first": "S",
"middle": [],
"last": "Arumugam",
"suffix": ""
}
],
"year": 2017,
"venue": "International Journal of Advances in Computer Science and Technology",
"volume": "3",
"issue": "",
"pages": "426--431",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Kalpana, R., Shanthi, N., & Arumugam, S. A survey on data mining techniques in agriculture, International Journal of Advances in Computer Science and Technology, vol. 3, pages. 426-431, 2017.",
"links": null
},
"BIBREF4": {
"ref_id": "b4",
"title": "Proposed algorithm of sentiment analysis for punjabi text",
"authors": [
{
"first": "A",
"middle": [],
"last": "Kaur",
"suffix": ""
},
{
"first": "V",
"middle": [],
"last": "Gupta",
"suffix": ""
}
],
"year": 2014,
"venue": "Journal of Emerging Technologies in Web Intelligence",
"volume": "6",
"issue": "",
"pages": "180--183",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Kaur, A., & Gupta, V. Proposed algorithm of sentiment analysis for punjabi text.Journal of Emerging Technologies in Web Intelligence, vol. 6, pages 180-183, 2014.",
"links": null
},
"BIBREF5": {
"ref_id": "b5",
"title": "Dictionary based sentiment analysis of hinglishtext",
"authors": [
{
"first": "H",
"middle": [],
"last": "Kaur",
"suffix": ""
},
{
"first": "V",
"middle": [],
"last": "Mangat",
"suffix": ""
},
{
"first": "N",
"middle": [],
"last": "Krail",
"suffix": ""
}
],
"year": 2017,
"venue": "International Journal of Advanced Research in Computer Science",
"volume": "8",
"issue": "",
"pages": "1--6",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Kaur, H., Mangat, V., & Krail, N. Dictionary based sentiment analysis of hinglishtext, International Journal of Advanced Research in Computer Science, vol. 8, pages 1-6, 2017.",
"links": null
},
"BIBREF6": {
"ref_id": "b6",
"title": "Text normalization of code mix and sentiment analysis",
"authors": [
{
"first": "S",
"middle": [],
"last": "Sharma",
"suffix": ""
},
{
"first": "P",
"middle": [
"Y K L"
],
"last": "Srinivas",
"suffix": ""
},
{
"first": "R",
"middle": [
"C"
],
"last": "Balabantaray",
"suffix": ""
}
],
"year": 2015,
"venue": "2015 international conference on advances in computing, communications and informatics",
"volume": "1",
"issue": "",
"pages": "1468--1473",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Sharma, S., Srinivas, P. Y. K. L., & Balabantaray, R. C. Text normalization of code mix and sentiment analysis, In 2015 international conference on advances in computing, communications and informatics, vol.1, pages 1468-1473, 2015.",
"links": null
},
"BIBREF7": {
"ref_id": "b7",
"title": "Sentiment analysis algorithms and applications: A survey",
"authors": [
{
"first": "W",
"middle": [],
"last": "Medhat",
"suffix": ""
},
{
"first": "A",
"middle": [],
"last": "Hassan",
"suffix": ""
},
{
"first": "H",
"middle": [],
"last": "Korashy",
"suffix": ""
}
],
"year": 2014,
"venue": "Ain Shams Eng. J",
"volume": "5",
"issue": "4",
"pages": "1093--1113",
"other_ids": {
"DOI": [
"10.1016/j.asej.2014.04.011"
]
},
"num": null,
"urls": [],
"raw_text": "W. Medhat, A. Hassan, and H. Korashy, Sentiment analysis algorithms and applications: A survey, Ain Shams Eng. J., no.4, vol. 5, pages 1093-1113, 2014, doi: 10.1016/j.asej.2014.04.011.",
"links": null
},
"BIBREF8": {
"ref_id": "b8",
"title": "Empirical evaluation and new design for fighting evolving twitter spammers",
"authors": [
{
"first": "Chao",
"middle": [],
"last": "Yang",
"suffix": ""
},
{
"first": "Robert",
"middle": [],
"last": "Harkreader",
"suffix": ""
},
{
"first": "Guofei",
"middle": [],
"last": "Gu",
"suffix": ""
}
],
"year": 2013,
"venue": "IEEE Transactions on Information Forensics and Security",
"volume": "8",
"issue": "",
"pages": "1280--1293",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Yang, Chao, Robert Harkreader, and Guofei Gu. Empirical evaluation and new design for fighting evolving twitter spammers. IEEE Transactions on Information Forensics and Security, vol. 8, pages 1280-1293, 2013.",
"links": null
}
},
"ref_entries": {
"TABREF1": {
"text": "",
"content": "<table/>",
"num": null,
"html": null,
"type_str": "table"
}
}
}
} |