Essec\Faculty\Model\Contribution {#2233
  #_index: "academ_contributions"
  #_id: "13134"
  #_source: array:26 [
    "id" => "13134"
    "slug" => "social-medias-impact-on-the-consumer-mindset-when-to-use-which-sentiment-extraction-tool"
    "yearMonth" => "2020-03"
    "year" => "2020"
    "title" => "Social Media's Impact on the Consumer Mindset: When to Use Which Sentiment Extraction Tool?"
    "description" => "KÜBLER, R., COLICEV, A. et PAUWELS, K. (2020). Social Media's Impact on the Consumer Mindset: When to Use Which Sentiment Extraction Tool? <i>Journal of Interactive Marketing</i>, 50(3), pp. 136-155."
    "authors" => array:3 [
      0 => array:3 [
        "name" => "KÜBLER Raoul"
        "bid" => "B00806952"
        "slug" => "kubler-raoul"
      ]
      1 => array:1 [
        "name" => "COLICEV Anatoli"
      ]
      2 => array:1 [
        "name" => "PAUWELS Koen"
      ]
    ]
    "ouvrage" => ""
    "keywords" => array:7 [
      0 => "Sentiment extraction"
      1 => "Consumer attitudes -Language dictionary -Maching learning"
      2 => "LIWC"
      3 => "Support vector machine"
      4 => "Brand strength"
      5 => "Volume"
      6 => "Valence -User generated content"
    ]
    "updatedAt" => "2023-01-27 01:00:44"
    "publicationUrl" => "https://doi.org/10.1016/j.intmar.2019.08.001"
    "publicationInfo" => array:3 [
      "pages" => "136-155"
      "volume" => "50"
      "number" => "3"
    ]
    "type" => array:2 [
      "fr" => "Articles"
      "en" => "Journal articles"
    ]
    "support_type" => array:2 [
      "fr" => "Revue scientifique"
      "en" => "Scientific journal"
    ]
    "countries" => array:2 [
      "fr" => null
      "en" => null
    ]
    "abstract" => array:2 [
      "fr" => "User-generated content provides many opportunities for managers and researchers, but insights are hindered by a lack of consensus on how to extract brand-relevant valence and volume. Marketing studies use different sentiment extraction tools (SETs) based on social media volume, top-down language dictionaries and bottom-up machine learning approaches. This paper compares the explanatory and forecasting power of these methods over several years for daily customer mindset metrics obtained from survey data. For 48 brands in diverse industries, vector autoregressive models show that volume metrics explain the most for brand awareness and purchase intent, while bottom-up SETs excel at explaining brand impression, satisfaction and recommendation. Systematic differences yield contingent advice: the most nuanced version of bottom-up SETs (SVM with Neutral) performs best for the search goods for all consumer mind-set metrics but Purchase Intent for which Volume metrics work best. For experienced goods, Volume outperforms SVM with neutral. As processing time and costs increase when moving from volume to top-down to bottom-up sentiment extraction tools, these conditional findings can help managers decide when more detailed analytics are worth the investment."
      "en" => "User-generated content provides many opportunities for managers and researchers, but insights are hindered by a lack of consensus on how to extract brand-relevant valence and volume. Marketing studies use different sentiment extraction tools (SETs) based on social media volume, top-down language dictionaries and bottom-up machine learning approaches. This paper compares the explanatory and forecasting power of these methods over several years for daily customer mindset metrics obtained from survey data. For 48 brands in diverse industries, vector autoregressive models show that volume metrics explain the most for brand awareness and purchase intent, while bottom-up SETs excel at explaining brand impression, satisfaction and recommendation. Systematic differences yield contingent advice: the most nuanced version of bottom-up SETs (SVM with Neutral) performs best for the search goods for all consumer mind-set metrics but Purchase Intent for which Volume metrics work best. For experienced goods, Volume outperforms SVM with neutral. As processing time and costs increase when moving from volume to top-down to bottom-up sentiment extraction tools, these conditional findings can help managers decide when more detailed analytics are worth the investment."
    ]
    "authors_fields" => array:2 [
      "fr" => "Marketing"
      "en" => "Marketing"
    ]
    "indexedAt" => "2024-11-21T09:21:53.000Z"
    "docTitle" => "Social Media's Impact on the Consumer Mindset: When to Use Which Sentiment Extraction Tool?"
    "docSurtitle" => "Articles"
    "authorNames" => "<a href="/cv/kubler-raoul">KÜBLER Raoul</a>, COLICEV Anatoli, PAUWELS Koen"
    "docDescription" => "<span class="document-property-authors">KÜBLER Raoul, COLICEV Anatoli, PAUWELS Koen</span><br><span class="document-property-authors_fields">Marketing</span> | <span class="document-property-year">2020</span>"
    "keywordList" => "<a href="#">Sentiment extraction</a>, <a href="#">Consumer attitudes -Language dictionary -Maching learning</a>, <a href="#">LIWC</a>, <a href="#">Support vector machine</a>, <a href="#">Brand strength</a>, <a href="#">Volume</a>, <a href="#">Valence -User generated content</a>"
    "docPreview" => "<b>Social Media's Impact on the Consumer Mindset: When to Use Which Sentiment Extraction Tool?</b><br><span>2020-03 | Articles </span>"
    "docType" => "research"
    "publicationLink" => "<a href="https://doi.org/10.1016/j.intmar.2019.08.001" target="_blank">Social Media's Impact on the Consumer Mindset: When to Use Which Sentiment Extraction Tool?</a>"
  ]
  +lang: "fr"
  +"_type": "_doc"
  +"_score": 8.554104
  +"parent": null
}