I wrote a script for this a while back to analyze them with nltk:

  #!/usr/bin/env python3
  
  import nltk
  import nltk.sentiment.vader
  import json
  import urllib.request
  
  companies = [
      "apple",
      "google",
      "microsoft",
      "amazon",
      "facebook",
  ]
  sample_size = 10
  
  
  def get_json(url):
      request = urllib.request.Request(url)
      request.add_header("User-Agent", "Python")
      return json.load(urllib.request.urlopen(request))
  
  
  def allcomments(json):
      comment = json["text"]
      comments = comment if comment else ""
      for child in json["children"]:
          comments += allcomments(child)
      return comments
  
  
  if __name__ == "__main__":
      for company in companies:
          sentiments = []
          print(company)
          for item in get_json("https://hn.algolia.com/api/v1/search?query=" + company + "&tags=story")["hits"][:sample_size]:
              tokens = nltk.tokenize.sent_tokenize(allcomments(get_json("https://hn.algolia.com/api/v1/items/" + item["objectID"])))
              sia = nltk.sentiment.vader.SentimentIntensityAnalyzer()
              scores = sum([sia.polarity_scores(token)["compound"] for token in tokens])
              sentiment = scores / len(tokens)
              print("\t" + item["title"] + ": " + str(sentiment))
              sentiments.append(sentiment)
          print("Average: " + str(sum(sentiments) / len(sentiments)))

Apple gets low scores because of China, mainly. Plus they lack the many open source projects that buoy up companies like Microsoft.