{"id":"https://openalex.org/W2464740699","doi":"https://doi.org/10.18653/v1/s16-1013","title":"thecerealkiller at SemEval-2016 Task 4: Deep Learning based System for Classifying Sentiment of Tweets on Two Point Scale","display_name":"thecerealkiller at SemEval-2016 Task 4: Deep Learning based System for Classifying Sentiment of Tweets on Two Point Scale","publication_year":2016,"publication_date":"2016-01-01","ids":{"openalex":"https://openalex.org/W2464740699","doi":"https://doi.org/10.18653/v1/s16-1013","mag":"2464740699"},"language":"en","primary_location":{"id":"doi:10.18653/v1/s16-1013","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/s16-1013","pdf_url":"https://www.aclweb.org/anthology/S16-1013.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.aclweb.org/anthology/S16-1013.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5059794281","display_name":"Vikrant Yadav","orcid":"https://orcid.org/0000-0003-2807-1276"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Vikrant Yadav","raw_affiliation_strings":["Amazon.in Hyderabad, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Amazon.in Hyderabad, India","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5059794281"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.5824,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":{"value":0.66812365,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"100","last_page":"102"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.9970999956130981,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9941999912261963,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/semeval","display_name":"SemEval","score":0.9380006194114685},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7879505157470703},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.7536755800247192},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.6534731388092041},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.6364034414291382},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.632614016532898},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5750479102134705},{"id":"https://openalex.org/keywords/sentiment-analysis","display_name":"Sentiment analysis","score":0.5454793572425842},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.0687088668346405},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.06252658367156982},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.05715540051460266}],"concepts":[{"id":"https://openalex.org/C44572571","wikidata":"https://www.wikidata.org/wiki/Q7448970","display_name":"SemEval","level":3,"score":0.9380006194114685},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7879505157470703},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.7536755800247192},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.6534731388092041},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.6364034414291382},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.632614016532898},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5750479102134705},{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.5454793572425842},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0687088668346405},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.06252658367156982},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.05715540051460266},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/s16-1013","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/s16-1013","pdf_url":"https://www.aclweb.org/anthology/S16-1013.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/s16-1013","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/s16-1013","pdf_url":"https://www.aclweb.org/anthology/S16-1013.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2464740699.pdf","grobid_xml":"https://content.openalex.org/works/W2464740699.grobid-xml"},"referenced_works_count":6,"referenced_works":["https://openalex.org/W1591801644","https://openalex.org/W2107878631","https://openalex.org/W2183112036","https://openalex.org/W2250243742","https://openalex.org/W2752201871","https://openalex.org/W2964199361"],"related_works":["https://openalex.org/W2117643817","https://openalex.org/W1988325893","https://openalex.org/W2751613946","https://openalex.org/W4287887314","https://openalex.org/W2803726945","https://openalex.org/W4291593823","https://openalex.org/W2805641541","https://openalex.org/W4287888941","https://openalex.org/W4221151255","https://openalex.org/W2955722679"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3],"propose":[4],"a":[5,14,21,35],"deep":[6],"learning":[7],"system":[8,79],"for":[9,69],"classification":[10],"of":[11,20,39,57,86],"tweets":[12,40,71],"on":[13,52,83],"two-point":[15],"scale.":[16],"Our":[17],"architecture":[18],"consists":[19],"multilayered":[22],"recurrent":[23,28],"neural":[24],"network":[25,31,64],"having":[26],"gated":[27],"units.":[29],"The":[30,63,77],"is":[32,49],"pre-trained":[33],"with":[34],"weakly":[36],"labeled":[37],"dataset":[38,56],"to":[41],"learn":[42],"the":[43,53,58,84],"sentiment":[44],"specific":[45],"embeddings.":[46],"Then":[47],"it":[48],"fine":[50],"tuned":[51],"given":[54],"training":[55],"task":[59,87],"4B":[60],"in":[61],"SemEval-2016.":[62],"does":[65],"very":[66],"little":[67],"pre-processing":[68],"raw":[70],"and":[72],"no":[73],"post-processing":[74],"at":[75],"all.":[76],"proposed":[78],"achieves":[80],"3rd":[81],"rank":[82],"leaderboard":[85],"4B.":[88]},"counts_by_year":[{"year":2019,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}