DOCSLIB.ORG

Pitch Perfect: Predicting Startup Funding Success Based on Shark Tank Audio

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Pitch Perfect: Predicting Startup Funding Success Based on Shark Tank Audio Pitch Perfect: Predicting Startup Funding Success Based on Shark Tank Audio Shubha Raghvendra Jeremy Wood Minna Xiao [email protected] [email protected] [email protected] Abstract In this paper we describe the design and evaluation of a neural network trained to distinguish between funded and unfunded venture capital pitches, particularly within the context of the television show Shark Tank. This work represents a novel ap- plication of existing research in the realm of emotion and persuasion detection and broader speech processing. After attempt- ing various architectures, including a sup- Figure 1: The Kang sisters, founders of Cof- port vector machine-based model, a recur- fee Meets Bagel, pitching on Shark Tank in rent neural net (RNN), and a convolutional 2015. neural net (CNN), we settled on on a hy- brid CNN-LSTM. Utilizing this optimal model, we were able to obtain validation accuracy of up to 68%. Given prior work The Emmy-award winning television show in the field and the challenges associated Shark Tank, which has been on the air since 2009, with this problem, the test accuracy pro- embodies made-for-TV venture capitalism. In any duced by our optimal model exceeded our given episode, several entrepreneurs pitch their expectation. This work demonstrates the ideas for a company to sharks (a panel of potential feasibility of applying speech features to investors) who decide whether or not to fund the gauge startup pitch quality, as well as the enterprise, and on what terms. While we initially utility of hybrid neural networks in rep- hoped to evaluate actual venture capital pitches, resenting the persuasiveness of small seg- perhaps from the records of a Silicon Valley firm, ments of speech data. given barriers to access to confidential early-stage information, we opted to evaluate publicly avail- 1 Introduction able Shark Tank pitches. To do so, we found sev- eral playlists on YouTube of each episode, and, 1.1 Motivation for some seasons of the show, segmented by in- Venture capital as a field has long struggled with dividual pitch and scraped the audio files asso- issues of diversity (Cutler, 2015). Because success ciated with each video. (For those seasons for in securing funding is largely a function of presen- which nicely segmented playlists did not exist, we tation quality, we were interested in understand- manually segmented them; our methodology is de- ing which specific aspects of a pitch predispose scribed below). We labeled this raw data with in- an entrepreneur to securing funding. Equipped formation about whether or not the venture was with such knowledge, minority founders could funded, and to what extent, with information tab- inch closer to equal footing in securing venture ulated about the show available online. Our ap- capital. proach is described in greater detail below. 1.2 Problem Statement 2 Background/Related Work Our goal was to understand which features of a Existing research in the realm of classifying au- startup pitch correspond to whether or not it was dio utterances has been conducted on the tasks funded in the context of Shark Tank. While we ini- of emotion recognition, personality identification, tially planned to segment based on precisely which and deception detection in speech. Prior to 2014, shark elected to fund an entrepreneur, in our sur- most research for classifying emotion in speech vey of relevant literature we found that even two- involved extracting prosodic (pitch, energy) and class problems in this realm were sufficiently chal- cepstral features (LPCC, MFCC) from the audio lenging. Thus we focused on refining our tech- and running them through a support vector ma- niques in the realm of binary classification for the chine (SVM). Pan et al. achieved a best recog- purposes of this project (Chernykh et al., 2017). nition rate of 90% on a small dataset consisting We planned to extract both raw audio and MFCC of 212 utterances of the Berlin Database of Emo- features (Han et al., 2006), as well as other emer- tional Speech (Emo-DB) for a three-class clas- gent speech features such as prosodic features. sification task for the emotions of sad, happy, (Agarwal et al., 2011; Schuller et al., 2016). and neutral (Pan et al., 2012). Experiments in- volving the Big-Five personality traits (Extrover- 1.3 Challenges sion, Agreeableness, Conscientiousness, Neuroti- cism, Openness) have also been performed using From the outset, we knew this would be a chal- MFCC and prosodic features with SVM classifiers lenging problem to tackle, and hence wanted to (Polzehl et al., 2010; Mohammadi and Vinciarelli, adjust our expectations accordingly. First, Shark 2012). Tank is a network television show whose ability to Recent work has begun introducing the appli- engage its audience is predicated on building sus- cation of neural network architectures toward the pense and injecting drama into the process of se- aforementioned problems. In 2014, Lee et al. lecting pitches to fund. Therefore, we expect the trained a bi-directional long short-term memory audio of the television show to attempt to obfus- (BLSTM) recurrent neural network on the Inter- cate the sharks’ ultimate decision on a pitch, mak- active Emotional Dyadic Motion Capture (IEMO- ing predicting outcomes a challenging undertak- CAP) database for four emotion classes (Lee and ing. Tashev, 2015), including F0, zero-crossing rate, Second, because not all seasons were available and MFCCs, which were then used as input for as segmented individual pitches, we had to manu- the two-hidden-layer BLSTM. Lee et al. achieved ally segment over one hundred episodes into indi- up to a 63% accuracy on the IEMO-CAP database. vidual pitches. This process was somewhat imper- In 2017, Chernykh et al. also performed utterance- fect given that occasionally a shark would unex- level classification on the IEMO-CAP database us- pectedly interject or ask a question, and that some ing an LSTM architecture (Chernykh et al., 2017). pitches involved gimmicks such as demos or per- They tried two approaches to training their net- formances. work: 1) one-label approach and 2) Connectionist Finally, technical problems in this realm have Temporal Classification (CTC) approach. In the been shown to be quite pernicious. For instance, one-label approach, each utterance has only one Chernykh et al. studied the efficacy of labeling emotional label regardless of the utterance length; utterances in the IEMO-CAP database (discussed in the CTC approach, the probability of a partic- below), and achieved a modest 54% accuracy on a ular labeling is added up from the probabilities of four-class classification problem (Chernykh et al., every alignment. The authors found the best re- 2017). Given that the utterances in IEMO-CAP sults with the CTC approach, achieving up to 54% are both much shorter in length than the pitches we accuracy on their four-class task. trained our model on, and that they were recorded in a much more controlled environment (without 3 Approach any background noise) with a set number of ac- 3.1 Dataset tors, we expected achieving a very high valida- tion accuracy to be fairly challenging (Busso et al., Our dataset consists of audio scraped from 2008). YouTube uploads of Shark Tank episodes, seg- mented by pitch. low rate of agreement amongst sharks, rendering To collect the data we need for labeling the this an even more exacting problem. pitches, we referenced a database cultivated by Halle Tecco, an angel investor, the founder of 3.2 Baseline Approach Rock Health, and a self-proclaimed “Shark Tank Motivated by existing research in emotion recog- fanatic. The database contains investment data nition and personality detection (Pan et al., 2012; from every season of Shark Tank, including the on- Polzehl et al., 2010; Mohammadi and Vinciarelli, going 8th season. For the purposes of our project, 2012), which use low-level feature extraction for we actually reached out to Ms. Tecco for guid- high-level classification tasks, we implemented a ance, and were able to access the entirety of her baseline binary support vector machine. database as a result. For each company that has pitched on the show, the database contains infor- 3.2.1 Mel-Frequency Cepstral Coefficients mation on the final deal terms for the product, We extracted the first 13-order mel-frequency cep- including amount, equity and valuation. Addi- stral coefficients for each frame of an input audio tionally, we have some supplementary information data segment. MFCC-based features are widely on the industry, entrepreneur gender, and which used in automatic speech recognition (ASR) tasks. sharks agreed to fund the company. Due to the limitations of the size of our dataset, we consciously attempted to reduce the dimensional- 3.1.1 Data Collection and Preprocessing ity of our feature vectors. Thus, instead of con- In order to collect the raw labeled audio, we wrote catenating all the MFCCs over an audio segment a scraper in Python using the youtube-dl pack- to create one large feature vector, we computed age to pull audio from pre-assembled playlists of the statistics of mean, standard deviation, median, Shark Tank pitches. We extracted audio clips from maximum, and minimum over the MFCC numbers these videos in the .wav format, which is widely of all the frames in an audio segment. supported by several Python packages, including TensorFlow. We then ran Mel-Frequency Cep- 3.2.2 Prosodic Features stral Coefficient (MFCC) feature extraction on our We also experimented with accounting for the raw audio files in order to prepare them for our prosodic features in our audio segments. Prosody, model (Han et al., 2006), which was necessary be- which refers to the aspect of speech not specific cause our inputs differed in elapsed time.
Load more

Recommended publications
  • Compliment Expressions and the Responses in Pitch Perfect Film
    PLAGIAT MERUPAKAN TINDAKAN TIDAK TERPUJI COMPLIMENT EXPRESSIONS AND THE RESPONSES IN PITCH PERFECT FILM A SARJANA PENDIDIKAN THESIS Presented as Partial Fulfilment of the Requirements to Obtain the Sarjana Pendidikan Degree in English Language Education By Devita Putri Saraswati Student Number: 141214079 ENGLISH LANGUAGE EDUCATION STUDY PROGRAM DEPARTMENT OF LANGUAGE AND ARTS EDUCATION FACULTY OF TEACHERS TRAINING AND EDUCATION SANATA DHARMA UNIVERSITY YOGYAKARTA 2018 i PLAGIAT MERUPAKAN TINDAKAN TIDAK TERPUJI A Sarjana Pendidikan Thesis on COMPLIMENT EXPRESSIONS AND THE RESPONSES IN PITCH PERFECT FILM By Devita Putri Saraswati Student Number: 141214079 Approved by Monica Ella Harendita, S.Pd., M.Ed. 15 March 2018 ii PLAGIAT MERUPAKAN TINDAKAN TIDAK TERPUJI A Sarjana Pendidikan Thesis on COMPLIMENT EXPRESSIONS AND THE RESPONSES IN PITCH PERFECT FILM By DEVITA PUTRI SARASWATI Student Number: 141214079 Defended before the Board of Examiners on 11 April 2018 and Declared Acceptable Board of Examiners Chairperson : Yohana Veniranda, S.Pd., M.Hum., M.A., Ph.D. ____________ Secretary : Christina Lhaksmita Anandari, S.Pd., Ed.M. ____________ Member : Monica Ella Harendita, S.Pd., M.Ed. ____________ Member : Priyatno Ardi, S.Pd., M.Hum. ____________ Member : Christina Lhaksmita Anandari, S.Pd., Ed.M. ____________ Yogyakarta, 11 April 2018 Faculty of Teachers Training and Education Sanata Dharma University Dean, Dr. Yohanes Harsoyo, S.Pd., M.Si iii PLAGIAT MERUPAKAN TINDAKAN TIDAK TERPUJI "A DREAM DOES NOT BECOME REALITY THROUGH MAGIC; IT TAKES SWEAT, DETERMINATION, AND HARD WORK." – Colin Powell I dedicate this thesis to those people who REFUSE TO GIVE UP on their dreams. iv PLAGIAT MERUPAKAN TINDAKAN TIDAK TERPUJI STATEMENT OF WORK’S ORIGINALITY I honestly declare that this thesis, which I have written, does not contain the work or parts of the work of other people, except those cited in quotations and the references, as scientific paper should.
    [Show full text]
  • Impoliteness Strategies in Pitch Perfect and Pitch Perfect 2 Movies
    IMPOLITENESS STRATEGIES IN PITCH PERFECT AND PITCH PERFECT 2 MOVIES A THESIS Submitted to Letters and Humanities Faculty in Partial Fulfillment of the Requirements for Degree Strata One (S1) DANTI YANIAR 1113026000028 ENGLISH LETTERS DEPARTMENT FACULTY OF LETTERS AND HUMANITIES SYARIF HIDAYATULLAH STATE ISLAMIC UNIVERSITY JAKARTA 2017 ABSTRACT Danti Yaniar, Impoliteness Strategies in Pitch Perfect and Pitch Perfect 2 Movies. Thesis. Jakarta: English Letters Department, Letters and Humanities Faculty, Syarif Hidayatullah State Islamic University of Jakarta, September 2017. This research is on Pragmatic scope that focuses on impoliteness strategies. The unit of analysis is Pitch Perfect movies transcript. It is a qualitative research. It is intended to identify the types of impoliteness strategies chosen by characters, how the characters respond the FTAs, and the features of offending event that trigger the characters to do impoliteness. This research used Impoliteness strategies theory by Jonathan Culpeper as the main theory, Impoliteness strategies by Derek Bousfield, and Offending Event by Timothy Jay as the supporting theories. As the result, it was found that all five strategies were chosen by the characters. The strategies from the most chosen to the less chosen were as follow positive impoliteness strategy, negative impoliteness strategy, bald on record impoliteness strategy, sarcasm or mock politeness strategy, and withhold politeness strategy. The characters frequently harmed the addressees‟ face by insulting name calling and using obscure language sub-strategies. The delivered FTAs made the addressees dominantly evaluated them with responses. The addressee responded the FTAs by choosing offensive countering. The characters mostly countered the offender by asking a challenge question which is in negative impoliteness strategy.
    [Show full text]
  • LAMORINDA WEEKLY | 'Pitch Perfect 2'
    LAMORINDA WEEKLY | 'Pitch Perfect 2' Published May 20th, 2015 'Pitch Perfect 2' By Derek Zemrak The pitch is in tune in "Pitch Perfect 2"! The Bella's are back in the sequel to the 2012 surprise hit, "Pitch Perfect," which was made on a $17 million production budget and grossed $65 million at the U. S. box office. Moviegoers knew a sequel would be in the works with those results. This time, everyone's favorite a capella group - the Barden University Bellas - must regain their glory after a humiliating wardrobe malfunction by Fat Amy (Rebel Wilson) that was witnessed by the president of the United States while they performed at the prestigious Lincoln Center for the Performing Arts. With nowhere else to turn, the Bellas enter an international competition that no American a cappella group has ever won. The solid ensemble cast from the original Fat Amy (Rebel Wilson) steals the show in movie returns in "Pitch Perfect 2," which includes "Pitch Perfect 2," the follow-up to 2012's Oscar nominee Anna Kendrick (Beca), comedian surprise hit. Photo Richard Cartwright Rebel Wilson (Fat Amy), Brittany Snow (Chloe), Ester Dean (Cynthia), Hana Mae Lee (Lily) and Alexis Knapp (Stacie). Beca is a senior at Barden and working as an intern at a record label company when Chloe discovers that Beca is more interested in her career. The Bellas once again must pull it all together and find the "perfect pitch." Two-time Emmy Award nominee Elizabeth Banks ("30 Rock") takes the directing reins and keeps the movie going at a quick, witty pace.
    [Show full text]
  • "So Very," "So Fetch": Constructing Girls on Film in the Era of Girl Power and Girls in Crisis
    Georgia State University ScholarWorks @ Georgia State University Institute for Women's, Gender, and Sexuality Women's, Gender, and Sexuality Studies Theses Studies 11-19-2008 "So Very," "So Fetch": Constructing Girls on Film in the Era of Girl Power and Girls in Crisis Mary Larken McCord Follow this and additional works at: https://scholarworks.gsu.edu/wsi_theses Recommended Citation McCord, Mary Larken, ""So Very," "So Fetch": Constructing Girls on Film in the Era of Girl Power and Girls in Crisis." Thesis, Georgia State University, 2008. https://scholarworks.gsu.edu/wsi_theses/13 This Thesis is brought to you for free and open access by the Institute for Women's, Gender, and Sexuality Studies at ScholarWorks @ Georgia State University. It has been accepted for inclusion in Women's, Gender, and Sexuality Studies Theses by an authorized administrator of ScholarWorks @ Georgia State University. For more information, please contact [email protected]. “SO VERY,” “SO FETCH”: CONSTRUCTING GIRLS ON FILM IN THE ERA OF GIRL POWER AND GIRLS IN CRISIS by MARY LARKEN MCCORD Under the Direction of Amira Jarmakani ABSTRACT In the mid-1990s, two discourses of girlhood emerged in both the popular and academic spheres. Consolidated as the girl power discourse and girls in crisis discourse, the tension between these two intertwined discourses created a space for new narratives of female adolescence in the decade between 1995 and 2005. As sites of cultural construction and representation, teen films reveal the narratives of girlhood. The films under consideration serve as useful exemplars for an examination of how such discourses become mainstreamed, pervading society’s image of female adolescence.
    [Show full text]
  • Pitch Perfect by Kay Cannon
    Pitch Perfect by Kay Cannon Revision by Jeff Roda Revision Date: 10/04/11 Date: 10/21/11 Based on the Book: "Pitch Perfect: The Quest for Collegiate A Capella Glory" By Micky Rapkin WHITE 10/21/11 BLUE REVISED 10/25/11 PINK PAGES 10/27/11 YELLOW PAGES 10/28/11 GREEN PAGES 11/1/11 GOLDENROD PAGES 11/16/11 BUFF PAGES 11/21/11 SALMON PAGES 11/27/11 CHERRY PAGES 12/12/11 TAN PAGES 12/13/11 SECOND REVISED BLUE 12/14/11 OVER BLACK: We hear the sound of a pitch pipe. VOICE One, two, three, four -- As the Universal logo appears on screen, we hear Universal’s theme song sung a cappella. FADE IN: 1 INT. LINCOLN CENTER - STAGE - CONTINUOUS 1 On stage, the Barden Treblemakers, an all-male a cappella group dressed in matching smoking jackets, conclude singing Universal’s theme song. BARDEN TREBLEMAKERS BRRMP BRRMP! The audience cheers. Then, the Trebles begin Rihanna’s, “Don’t Stop The Music,” and the place goes apeshit. BARDEN TREBLEMAKERS (CONT’D) PLEASE DON’T STOP THE MUSIC, MUSIC, MUSIC/PLEASE DON’T STOP THE MUSIC, MUSIC... (song continues) 2 INT. DRESSING ROOM - NIGHT 2 CREDITS ROLL as a gorgeous girl, CHLOE (20), dressed like a 1970’s flight attendant, frantically rummages through a swanky dressing room. CHLOE Shitballs, where is it? Chloe stops, reaches into her jacket pocket, and pulls out a red scarf. Relieved, she DASHES out of the room. 3 INT. STAGE - SAME TIME 3 As the BU Treblemakers sing, we WIDEN TO REVEAL: A sold out audience at Lincoln Center.
    [Show full text]
  • Sing. Dance. Laugh. Repeat!
    Sing. Dance. Laugh. Repeat! BRING HOME HAPPY WITH DREAMWORKS TROLLS ON DIGITAL HD JAN. 24 AND BLU-RAY™ & DVD FEB. 7 DREAMWORKS TROLLS PARTY EDITION INCLUDES NEVER-BEFORE-SEEN SING-ALONG, DANCE-ALONG “PARTY MODE”, AND IS PACKED WITH FUN, COLORFUL EXTRAS, PERFECT FOR YOUR FAMILY MOVIE NIGHT! LOS ANGELES, CA (Jan. 10, 2016) – A hit with audiences and critics alike, DreamWorks Animation’s TROLLS, is the “feel-good” movie of the year receiving an outstanding audience reaction with a coveted “A” CinemaScore® and a Certified Fresh rating on RottenTomatoes.com. DreamWorks TROLLS, the fresh, music- filled adventure packed with humor and heart dances onto Digital HD on Jan. 24 and Blu-ray™ and DVD on Feb. 7 from Twentieth Century Fox Home Entertainment. The colorful, richly textured Troll Village is full of optimistic Trolls, who are always ready to sing, dance and party. When the comically pessimistic Bergens invade, Poppy (Anna Kendrick), the happiest Troll ever born, and the overly-cautious, curmudgeonly Branch (Justin Timberlake) set off on an epic journey to rescue her friends. Their mission is full of adventure and mishaps, as this mismatched duo try to tolerate each other long enough to get the job done. From Shrek franchise veterans Mike Mitchell (Shrek Forever After) and Walt Dohrn (Shrek 2, Shrek the Third), TROLLS features the voice talents of Golden Globe® nominee Anna Kendrick (Pitch Perfect, The Accountant), Grammy and Emmy® Award winner and Golden Globe® Award nominee Justin Timberlake (Friends with Benefits, Shrek the Third), Golden
    [Show full text]
  • TOTAL VOCAL with Deke Sharon
    03-25 DCINY.qxp_GP 3/15/18 4:15 PM Page 1 Sunday Afternoon, March 25, 2018, at 2:00 Celebrating DCINY’s 10th Anniversary Season! Iris Derke, Co-Founder and General Director Jonathan Griffith, Co-Founder and Artistic Director presents TOTAL VOCAL with Deke Sharon Deke Sharon , Guest Conductor, Arranger, & Creative Director Shelley Regner , Guest Soloist Matthew Sallee , Guest Soloist VoicePlay , Special Guests Shemesh Quartet (Mexico) , Special Guests Distinguished Concerts Singers International All arrangements by Deke Sharon unless otherwise noted TOBIAS BOSHELL I’ve Got The Music in Me (from The Sing-Off ) Distinguished Concerts International Soloists: LYDIA BROWELL, GABRIELLA DERKE, ALEXIS DILUCIA, KAT HAMMOCK, MARY LASTER, CHLOE MESOGITIS, SARA PARKER, ALEXANDRA PRODANUK, CARSON RICHMOND, HANNAH SALLEY BOBBY TROUP Route 66 (from Groove 66 ) CATHY DENNIS, HENRIK JONBACK, PONTUS WINNBERG, CHRISTIAN KARLSSON Toxic (from Pitch Perfect 3 ) JESSE HARRIS, NORAH JONES Don’t Know Why ROOM 100, PETERS TOWNSHIP HIGH SCHOOL, Featured Ensemble JOHN FOGERTY Proud Mary TONY VELONA Lollipops And Roses ITTAI MAZOR, BRUNO CISNEROS, SARA GONZALES, RUBÉN BLADES, GILBERTO SANTA ROSA Latin Medley SHEMESH QUARTET, Featured Ensemble David Geffen Hall Please make certain your cellular phone, pager, or watch alarm is switched off. 03-25 DCINY.qxp_GP 3/15/18 4:15 PM Page 2 RANDY NEWMAN When She Loved Me (from D Cappella ) DUFFY, STEVE BOOKER Mercy EVOLVE, CHESAPEAKE HIGH SCHOOL, Featured Ensemble LOUIS PRIMA Sing, Sing, Sing LIONEL RICHIE All Night Long (from
    [Show full text]
  • PP2 SHOOTING DRAFT.Fdx
    "PITCH PERFECT 2" Written by Kay Cannon Based on the book by Mickey Rapkin This material is the property of UNIVERSAL PICTURES and is intended for us only by authorized personnel. Distribution of disclosure of this material to unauthorized persons is prohibited. The sale, copying, or reproduction of this material in any form or medium, in whole or in part, is strictly prohibited. As the Universal logo appears on screen, we hear Universal’s theme song being sung a cappella by... 1 INT. KENNEDY CENTER - PRESS BOX 1 Our a cappella commentators, JOHN and GAIL. JOHN Excited, Gail? GAIL If I weren’t, would I be wearing a diaper under this dress? As John speaks, we HEAR a marching band play. JOHN (V.O.) Welcome back, a cappella enthusiasts! 2 INT. KENNEDY CENTER - STAGE - AUGUST 2014 - NIGHT 2 A MILITARY BAND heads off stage as THE BARDEN BELLAS get set. JOHN I’m John Smith and sitting to my right is Gail Abernathy-McCadden- Feinberger. GAIL (pointing to wedding ring) This one’s gonna stick, John. JOHN Well you saved the Jew for last. GAIL (gleefully nodding) I did, I did. JOHN You’re listening to Let’s Talk- appella, the world’s premiere downloadable a cappella podcast. GAIL We are coming to you live from our nation’s capitol where the Barden University Bellas are about to rock the historic Kennedy Center. (CONTINUED) "Pitch Perfect 2" SHOOTING DRAFTS 5-11-14 2. 2 CONTINUED: 2 The BELLAS: BECA, CHLOE, Lilly, STACIE, CYNTHIA ROSE, JESSICA, ASHLEY and a small Latina girl, FLORENCIA FUENTES (FLO).
    [Show full text]
  • 1. Mean Girls 2. Bridesmaids 3. Breakfast at Tiffany's 4. Mama Mia! 5. the Notebook 6. Dirty Dancing 7. How to Lose a Guy in 1
    1. Mean Girls 51. The Princess Bride 2. Bridesmaids 52. She’s All That 3. Breakfast at Tiffany’s 53. Friends With Benefits 4. Mama Mia! 54. Forgetting Sarah Marshall 5. The Notebook 55. Penelope 6. Dirty Dancing 56. Letters to Juliet 7. How to Lose a Guy in 10 Days 57. The Last Song 8. Titanic 58. Charlie’s Angels 9. Pitch Perfect 59. 17 Again 10. The Breakfast Club 60. My Sister’s Keeper 11. 13 Going on 30 61. My Girl 12. John Tucker Must Die 62. The Bounty Hunter 13. Leap Year 63. Date Night 14. P.S. I Love You 64. Sex In the City 15. Set It Up 65. Knocked Up 16. 27 Dresses 66. Life as We Know It 17. The Proposal 67. Never Been Kissed 18. Just Friends 68. Failure to Launch 19. The Devil Wears Prada 69. Confessions of a Shopaholic 20. Me Before You 70. Pretty in Pink 21. Legally Blonde 71. What a Girl Wants 22. Clueless 72. Walk the Line 23. She’s the Man 73. The Heat 24. The Lizzie McGuire Movie 74. Marley & Me 25. Bride Wars 75. Crazy Stupid Love 26. Safe Haven 76. Moulin Rouge 27. The Parent Trap 77. He’s Just Not that into You 28. 50 First Dates 78. How to be Single 29. Charlie St. Cloud 79. Fifty Shades of Grey 30. When In Rome 80. Twilight Saga 31. Grease 81. Coyote Ugly 32. A Walk to Remember 82. This Means War 33. The Sisterhood of the Traveling Pants 83.
    [Show full text]
  • Spirited Away Bus174 End of the Century How to Train Your Dragon
    Spirited Away Bus174 End of the Century How to Train Your Dragon 2 My Architect The Rocket The Prestige Best Worst Movie Gangs of New York 2 Days in Paris The Blue Room Jay and Silent Bob Strike Back Bruce Almighty Les Parapluises de Cherbourg Bloody Sunday Deep Water High Fidelity MI Rogue Nation The Imposter The man on the train Samson and Deliah The Master Iron Man 3 Shutter Insidious Paper Heart A Separation Argo Call Me Kuchu Finding Vivian Maier March of the Penguins The Descendants The Host Despicable Me Wendy and Lucy The One I Love The Matador Grabbers Bug The Best of Youth Amores Perros The king of Devil's Island Elite Squad Lincoln Precious The Host Chef Fill the Void The Best Exotic Marigold Hotel Ted Dead Snow Anger Management The Two Towers The Man Without a Past West of Memphis Bridge of Spies Hugo Moon Summer Hours The World's End Coherence Secretary Life Aquatic 17 Again The Ladykillers Pans Labyrinth The Crash Reel Volver Birdman Good Night and Good Luck Into the Wild Spy The World's End Rise of the Planets of the Apes Secretary Klown THem Quarantine Fellowship of the ring The Bourne Ultimatum Timbuktu Best of enemies Bowling for Columbine Headhunters Gomorra The Way, Way Back Hachi: A Dog's Tale Room 237 A.I. Passion of the christ Funny Games The Look of Silence The Beaches of Agness Shrek Adaptation Batman Begins Hairspray Enchanted The Skin I live IN Uncle Boonmee Chuck & Buck Taken Evil Dead antiviral Sherpa Selma Shaun of the Dead Saraband A Band called death Girlhood Blue Jasmine The Italian Spider Man American Psycho Old School Suicide Club The Ice Harvest Spotlight Mad Max: Fury Road Oslo.
    [Show full text]
  • Analysis of Deixis in Pitch Perfect 2 Movie
    Jurnal Ilmu Budaya Vol. 3, No. 3, Juli 2019 e-ISSN 2549-7715 Hal: 288-302 ANALYSIS OF DEIXIS IN PITCH PERFECT 2 MOVIE Kholifatul Ainiyah, Surya Sili, Setya Ariani Department of English Literature, Faculty of Cultural Sciences, Mulawaman University E-mail: [email protected] ABSTRACT This study aimed to find out the types of deixis and contexts used by Beca Mitchell character in Pitch Perfect 2 movie. In order to fulfill the two aims, this study used two theories: Levinson’s (1983) five types of deixis were used to find out the types of deixis, while Panevová and Hana’s (2011) four types of context were used to find out the types of context appeared in each utterance of Beca Mitchell character. This study was a qualitative research with content analysis as its approach. Moreover, this study used words as its data in the forms of narrations and dialogues uttered by Beca Mitchell character taken from Pitch Perfect 2 movie script. The results of this study showed there were five types of deixis used by Beca Mitchell character: person deixis (40% of first person, 23% of second person, and 6% of third person), time deixis usage as 12%, place deixis usage as 6% shown in both types: proximal and distal, discourse deixis usage as 6%, and social deixis usage as 7% shown only in the relational type that considered the closeness of relationship between Beca and her hearers. Meanwhile, there were four types of context appeared: physical, linguistic, social, and epistemic. The conclusions showed that Beca Mitchell used Levinson’s five types of deixis along with four types of context by Panevová and Hana to help her getting clearer about things she said in her utterances.
    [Show full text]
  • X-Phemism Formation Strategies of Taboo Expression in Pitch Perfect 2
    X-PHEMISM FORMATION STRATEGIES OF TABOO EXPRESSION IN PITCH PERFECT 2 An Undergraduate Thesis Submitted to Letters and Humanities Faculty In Partial Fulfillment of the Requirements for The Degree of Strata One ARCCIASA JUNANDA SIHADIHARJO NIM. 11140260000027 ENGLISH LETTERS DEPARTMENT LETTERS AND HUMANITIES FACULTY STATE ISLAMIC UNIVERSITY OF JAKARTA JAKARTA 2018 ABSTRACT Arcciasa Junanda Sihadiharjo, X-Phemism Formation Strategies of Taboo Expression in Pitch Perfect 2. Thesis: English Letters Department, Letters and Humanities Faculty, State Islamic University Syarif Hidayatullah Jakarta, 2018. The aim of this research is to analyse the content that forming strategies of X-Phemism in a film entitled Pitch Perfect 2. The content means here is the elements of taboo expression such as motive, function, and emotive context of the taboo expression that helps arrange the taboo happens in the certain context of the conversation. It is a qualitative research based on the theory of Allan and Burridge in Andrea Pizzaro (2018) to find the type of taboo, to find out the motive of taboo the researcher using Ulmann‟s Theory in JJ. Calvo (2005), then to analyze the function of taboo this study is using Searle's theory in the journal Ordenes and Ludwig (2016) about speech act, Finally, to find out the emotive context in the film Pitch Perfect 2, it uses the foundation of the journals Rosenberg and McDonald, Dethier (2014) and Du, Yong Tao and Aleix (2014). The concept of motives, function and emotive context then being elaborated to conceptualize the forming of X-Phemism strategies to understand and classify the way speaker talking about taboo expression wether Euphemism, Dysphemism, or Orthophemism.
    [Show full text]