In　the　last　ten　years,　social　media　site　mining,　including　Twitter,　Facebook,　Instagram,　and　all　other　websites,　has　become　a　popular　area　of　study.　In　the　world　of　digital　media　today,　people　are　getting　more　and　more　vocal　because　they　love　to　share　their　opinions.　User-generated　material　abounds　on　social　media　platforms　and　apps　such　as　Facebook,　WhatsApp,　and　Twitter,　providing　wealthy　content　to　gather　sentiments.　Comments　are　another　way　that　the　most　active　and　social　voices　can　make　themselves　heard,　which　in　turn　gives　us　a　window　into　their　feelings.　Sentiment　analysis　is　the　task　of　comprehending　the　emotions　and　opinions　conveyed　in　text　and　other　media.　It　has　various　applications　in　domains　along　with　social　media　such　as　e-commerce,　health,　politics,　and　marketing.　This　paper　delineates　the　generic　process　of　sentiment　analysis　and　reviews　the　main　methods,　challenges,　and　trends　in　this　field.　The　main　goal　of　this　survey　paper　is　to　survey　the　current　state-of-the-art　research　works　on　sentiment　analysis　(SA)　techniques　and　related　fields　and　to　compare　the　performance　of　different　deep　learning　models　for　sentiment　polarity.　The　paper　also　discusses　the　recent　studies　that　have　employed　machine　learning,　deep　learning,　and　hybrid　models　to　address　sentiment　polarity　problems,　which　is　the　categorization　of　text　into　positive,　negative,　or　neutral　sentiments.　The　paper　evaluates　the　results　of　different　models　on　a　series　of　datasets.　The　paper’s　main　contributions　are　the　elaborate　classifications　of　numerous　recent　articles　and　the　demonstration　of　the　recent　research　directions　in　sentiment　analysis　and　its　related　fields.　The　paper　aims　to　provide　a　comprehensive　overview　of　SA　techniques　with　succinct　details.　©　The　Author(s),　under　exclusive　licence　to　Springer-Verlag　London　Ltd.,　part　of　Springer　Nature　2025.